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# Content
1. [[Scene Reconstruction](#Scene_Reconstruction)]
2. [[3DGS Reconstructed Scene](#3DGS_Reconstructed_Scene)]
3. [[Scene Understanding](#Scene_Understanding)]
4. [[(i) Per-Scene Optimization-based Methods](#Per_Scene_Optimization_based_Methods)]
5. [[(ii) Per-Scene Optimization-Free Methods](#Per_Scene_Optimization_Free_Methods)]
6. [[NeurIPS 2025](#NeurIPS_2025)]
7. [[ICLR 2026](#ICLR_2026)]
8. [[Point cloud processing](#Point_cloud_processing)]
9. [[3D Fusion](#3D_Fusion)]

-----------------------------------------------------------------------------------------------
1. [[Remote Sensing](#Remote_Sensing)]
2. [[Training Free Segmentation](#Training_Free)]            
3. [[Zero shot Classification/Test Time Adaptation](#Zero_shot_classification)]      
4. [[Optimal Transport](#Optimal_Transport)]     
5. [[VLMs and MLLM](#VLMs)]           
6. [[Visual Place Recognition](#VPR)]         
7. [[Token Mering, Clustering and Pruning](#Token_fusion)]           
8. [[Backbone](#Backbone)]           
9. [[Weakly Supervised Semantic Segmentation](#WSSS)]           
10. [[Open vocabulary](#open_vocabulary)]                  
11. [[segmentation and detection](#Segmentation_and_Detection)]
12. [[Active learning/ Data Selection](#Active)]
13. [[Time series](#Time_series)]
14. [[Deep Estimation](#Deep_Estimation)]
15. [[Model Merging](#Model_Merging)]  
16. [[Other](#Other)]      
-----------------------------------------------------------------------------------------------

<a name="Scene_Reconstruction"></a>  
## Scene Reconstruction
1. [2024 CVPR] **DUSt3R: Geometric 3D Vision Made Easy** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=DUSt3R%3A+Geometric+3D+Vision+Made+Easy&btnG=) [[code]](https://github.com/naver/dust3r)
2. [2025 arXiv] **π³: Permutation-Equivariant Visual Geometry Learning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=%CF%80%C2%B3%3A+Permutation-Equivariant+Visual+Geometry+Learning&btnG=) [[code]](https://github.com/yyfz/Pi3)
3. [2025 CVPR] **Align3R: Aligned Monocular Depth Estimation for Dynamic Videos** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=*Align3R%3A+Aligned+Monocular+Depth+Estimation+for+Dynamic+Videos&btnG=) [[code]](https://github.com/jiah-cloud/Align3R)
4. [2025 CVPR] **Continuous 3D Perception Model with Persistent State** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Continuous+3D+Perception+Model+with+Persistent+State&btnG=) [[code]](https://github.com/CUT3R/CUT3R)
5. [2025 CVPR] **Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Fast3R%3A+Towards+3D+Reconstruction+of+1000%2B+Images+in+One+Forward+Pass&btnG=) [[code]](https://github.com/facebookresearch/fast3r)
6. [2025 CVPR] **MapAnything: Universal Feed-Forward Metric 3D Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MapAnything%3A+Universal+Feed-Forward+Metric+3D+Reconstruction&btnG=) [[code]](https://github.com/facebookresearch/map-anything)
7. [2025 CVPR] **MUSt3R: Multi-view Network for Stereo 3D Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MUSt3R%3A+Multi-view+Network+for+Stereo+3D+Reconstruction&btnG=) [[code]](https://github.com/naver/must3r)
8. [2025 CVPR] **MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MV-DUSt3R%2B%3A+Single-Stage+Scene+Reconstruction+from+Sparse+Views+In+2+Seconds&btnG=) [[code]](https://github.com/facebookresearch/mvdust3r)
9. [2025 CVPR] **Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Pow3R%3A+Empowering+Unconstrained+3D++Reconstruction+with+Camera+and+Scene+Prior&btnG=) [[code]](https://github.com/naver/pow3r)
10. [2025 CVPR] **VGGT: Visual Geometry Grounded Transformer** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VGGT%3A+Visual+Geometry+Grounded+Transformer&btnG=) [[code]](https://github.com/facebookresearch/vggt)
11. [2025 ICLR] **MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MonST3R%3A+A+Simple+Approach+for+Estimating+Geometry+in+the+Presence+of+Motion&btnG=) [[code]](https://github.com/Junyi42/monst3r)
12. [2025 NeurIPS] **SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SIU3R%3A+Simultaneous+Scene+Understanding+and+3D+Reconstruction+Beyond+Feature+Alignment&btnG=) [[code]](https://github.com/WU-CVGL/SIU3R)
13. [2025 ICCV] **Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Amodal3R%3A+Amodal+3D+Reconstruction+from+Occluded+2D+Images&btnG=) [[code]](https://github.com/Sm0kyWu/Amodal3R)
14. [2025 arXiv] **VGD: Visual Geometry Gaussian Splatting for Feed-Forward Surround-view Driving Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VGD%3A+Visual+Geometry+Gaussian+Splatting+for+Feed-Forward+Surround-view+Driving+Reconstruction&btnG=)
15. [2025 ACM] **LVT: Large-Scale Scene Reconstruction via Local View Transformers** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LVT%3A+Large-Scale+Scene+Reconstruction+via+Local+View+Transformers&btnG=)
16. [2025 NeurIPS] **Styl3R: Instant 3D Stylized Reconstruction for Arbitrary Scenes and Styles** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Styl3R%3A+Instant+3D+Stylized+Reconstruction+for+Arbitrary+Scenes+and+Styles&btnG=) [[code]](https://github.com/WU-CVGL/Styl3R)
17. [2025 arXiv] **OmniVGGT: Omni-Modality Driven Visual Geometry Grounded** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OmniVGGT%3A+Omni-Modality+Driven+Visual+Geometry+Grounded&btnG=) [[code]](https://github.com/Livioni/OmniVGGT-offcial)
18. [2025 arXiv] **Depth Anything 3: Recovering the Visual Space from Any Views** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Depth+Anything+3%3A+Recovering+the+Visual+Space+from+Any+Views&btnG=) [[code]](https://github.com/ByteDance-Seed/depth-anything-3)
19. [2025 arXiv] **AMB3R: Accurate Feed-forward Metric-scale 3D Reconstruction with Backend** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=AMB3R%3A+Accurate+Feed-forward+Metric-scale+3D+Reconstruction+with+Backend&btnG=) [[code]](https://github.com/HengyiWang/amb3r)
20. [2025 arXiv] **Emergent Outlier View Rejection in Visual Geometry Grounded Transformers** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Emergent+Outlier+View+Rejection+in+Visual+Geometry+Grounded+Transformers&btnG=) [[code]](https://github.com/cvlab-kaist/RobustVGGT)
21. [2025 arXiv] **VGGT4D: Mining Motion Cues in Visual Geometry Transformers for 4D Scene Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VGGT4D%3A+Mining+Motion+Cues+in+Visual+Geometry+Transformers+for+4D+Scene+Reconstruction&btnG=) [[code]](https://github.com/3DAgentWorld/VGGT4D)
22. [2025 arXiv] **4DLangVGGT: 4D Language Visual Geometry Grounded Transformer** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=4DLangVGGT%3A+4D+Language+Visual+Geometry+Grounded+Transformer&btnG=) [[code]](https://github.com/hustvl/4DLangVGGT)
23. [2025 arXiv] **GVLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GVLM%3A+Geometry+Grounded+Vision+Language+Model+with+Unified+3D+Reconstruction+and+Spatial+Reasoning&btnG=) [[code]](https://github.com/InternRobotics/G2VLM)
24. [2025 arXiv] **PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PhysX-Anything%3A+Simulation-Ready+Physical+3D+Assets+from+Single+Image&btnG=) [[code]](https://github.com/ziangcao0312/PhysX-Anything)
25. [2025 arXiv] **PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PAGE-4D%3A+Disentangled+Pose+and+Geometry+Estimation+for+4D+Perception&btnG=) [[code]](https://page-4d.github.io/anonymous-submission/)
26. [2025 CVPR] **Feat2GS: Probing Visual Foundation Models with Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Feat2GS%3A+Probing+Visual+Foundation+Models+with+Gaussian+Splatting&btnG=) [[code]](https://github.com/fanegg/Feat2GS)
27. [2025 arXiv] **IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=IGGT%3A+Instance-Grounded+Geometry+Transformer+for+Semantic+3D+Reconstruction&btnG=) [[code]](https://github.com/lifuguan/IGGT_official)
28. [2026 arXiv] **V-DPM: 4D Video Reconstruction with Dynamic Point Maps** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=V-DPM%3A+4D+Video+Reconstruction+with+Dynamic+Point+Maps&btnG=) [[code]](https://github.com/eldar/vdpm)
29. [2025 arXiv] **PanSt3R: Multi-view Consistent Panoptic Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PanSt3R%3A+Multi-view+Consistent+Panoptic+Segmentation&btnG=)
30. [2025 NeurIPS] **RaySt3R: Predicting Novel Depth Maps for Zero-Shot Object Completion** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=RaySt3R%3A+Predicting+Novel+Depth+Maps+for+Zero-Shot+Object+Completion&btnG=) [[code]](https://github.com/Duisterhof/rayst3r)
31. [2026 arXiv] **VGGT-SLAM 2.0: Real time Dense Feed-forward Scene Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VGGT-SLAM+2.0%3A+Real+time+Dense+Feed-forward+Scene+Reconstruction&btnG=)
32. [2025 arXiv] **G3Splat: Geometrically Consistent Generalizable Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=G3Splat%3A+Geometrically+Consistent+Generalizable+Gaussian+Splatting&btnG=) [[code]](https://github.com/m80hz/g3splat)
33. [2026 CVPR] **SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SeeThrough3D%3A+Occlusion+Aware+3D+Control+in+Text-to-Image+Generation&btnG=) [[project]](https://seethrough3d.github.io/)
34. [2026 CVPR] **Speed3R: Sparse Feed-forward 3D Reconstruction Models** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Speed3R%3A+Sparse+Feed-forward+3D+Reconstruction+Models&btnG=) [[code]](https://github.com/Visual-AI/speed3r)
35. [2026 ICLR] **SCENETRANSPORTER: Optimal Transport-Guided Compositional Latent Diffusion for Single-Image Structured 3D Scene Generation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SCENETRANSPORTER%3A+OPTIMAL+TRANSPORT-GUIDED+COMPOSITIONAL+LATENT+DIFFUSION+FOR+SINGLEIMAGE+STRUCTURED+3D+SCENE+GENERATION&btnG=) [[project]](https://2019epwl.github.io/SceneTransporter/)
36. [2026 CVPR] **VGG-T³ Offline Feed-Forward 3D Reconstruction at Scale** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VGG-T%C2%B3+Offline+Feed-Forward+3D+Reconstruction+at+Scale&btnG=) [[project]](https://research.nvidia.com/labs/dvl/projects/vgg-ttt/)
37. [2025 arXiv] **VGGT-Long: Chunk it, Loop it, Align it -- Pushing VGGT's Limits on Kilometer-scale Long RGB Sequences** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VGGT-Long%3A+Chunk+it%2C+Loop+it%2C+Align+it+--+Pushing+VGGT%27s+Limits+on+Kilometer-scale+Long+RGB+Sequences&btnG=) [[code]](https://github.com/DengKaiCQ/VGGT-Long)
38. [2026 CVPR] **RnG: A Unified Transformer for Complete 3D Modeling from Partial Observations** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=RnG%3A+AUnified+Transformer+for+Complete+3D+Modeling+from+Partial+Observations&btnG=) [[code]](https://github.com/XiangMochu/RnG)
39. [2026 arXiv] **InSpatio-WorldFM: An Open-Source Real-Time Generative Frame Model** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=InSpatio-WorldFM%3A+An+Open-Source+Real-Time+Generative+Frame+Model&btnG=) [[project]](https://inspatio.github.io/worldfm/)
40. [2026 CVPR] **PanoVGGT: Feed-Forward 3D Reconstruction from Panoramic Imagery** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PanoVGGT%3A+Feed-Forward+3D+Reconstruction+from+Panoramic+Imagery&btnG=) [[code]](https://github.com/YijingGuo-June/PanoVGGT)
41. [2026 CVPR] **Pe3r: Perception-efficient 3D Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PE3R%3A+Perception-Efficient+3D+Reconstruction&btnG=) [[code]](https://github.com/hujiecpp/PE3R)
42. [2026 arXiv] **OVGGT: O(1) Constant-Cost Streaming Visual Geometry Transformer** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OVGGT%3A+O%281%29+Constant-Cost+Streaming+Visual+Geometry+Transformer&btnG=) [[project]](https://vaisr.github.io/OVGGT/)
43. [2026 arXiv] **LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LoGeR%3A+Long-Context+Geometric+Reconstruction+with+Hybrid+Memory&btnG=) [[project]](https://loger-project.github.io/)
44. [2026 ICLR] **FastVGGT: Training-Free Acceleration of Visual Geometry Transformer** [[paper]](https://arxiv.org/abs/2509.02560) [[project]](https://github.com/mystorm16/FastVGGT)
45. [2026 ICLR] **Streaming 4D Visual Geometry Transformer** [[paper]](https://arxiv.org/abs/2507.11539) [[project]](https://github.com/wzzheng/streamvggt)
46. [2026 ----] **DA3-Streaming: Memory-Efficient Inference for Videos via Chunk Streaming** [[paper]](https://github.com/ByteDance-Seed/Depth-Anything-3/blob/main/da3_streaming/README.md) [[project]](https://github.com/ByteDance-Seed/Depth-Anything-3/blob/main/da3_streaming/README.md)

<a name="3DGS_Reconstructed_Scene"></a>  
### 3DGS Reconstructed Scene
1. [2025 ACM] **Casual3DHDR: High Dynamic Range 3D Gaussian Splatting from Casually Captured Videos** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Casual3DHDR%3A+High+Dynamic+Range+3D+Gaussian+Splatting+from+casually-captured+Videos&btnG=) [[code]](https://github.com/WU-CVGL/CasualHDRSplat)
2. [2025 arXiv] **VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VIGS-SLAM%3A+Visual+Inertial+Gaussian+Splatting+SLAM&btnG=) [[code]](https://vigs-slam.github.io/)
3. [2026 arXiv] **GaussExplorer: 3D Gaussian Splatting for Embodied Exploration and Reasoning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GaussExplorer%3A+3D+Gaussian+Splatting+for+Embodied+Exploration+and+Reasoning&btnG=) [[code]](https://vigs-slam.github.io/)
4. [2025 ICRA] **Hier-slam: Scaling-up semantics in slam with a hierarchically categorical gaussian splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Hier-SLAM%3A+Scaling-up+Semantics+in+SLAM+with+a+Hierarchically+Categorical+Gaussian+Splatting&btnG=) [[code]](https://github.com/LeeBY68/Hier-SLAM)
5. [2026 arXiv] **EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=EVolSplat4D%3A+Efficient+Volume-based+Gaussian+Splatting+for+4D+Urban+Scene+Synthesis&btnG=) [[code]](https://xdimlab.github.io/EVolSplat4D/)
6. [2025 CVPR] **NexusGS: Sparse View Synthesis with Epipolar Depth Priors in 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=NexusGS%3A+Sparse+View+Synthesis+with+Epipolar+Depth+Priors+in+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/USMizuki/NexusGS)
7. [2026 arXiv] **SplatBus: A Gaussian Splatting Viewer Framework via GPU Interprocess Communication** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SplatBus%3A+A+Gaussian+Splatting+Viewer+Framework+via+GPU+Interprocess+Communication&btnG=) [[code]](https://github.com/RockyXu66/splatbus)
8. [2026 ICRL] **Mesh Splatting for End-to-end Multiview Surface Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MESH+SPLATTING+FOR+END+-+TO+-+END+MULTIVIEW+SURFACE+RECONSTRUCTION&btnG=)
9. [2025 arXiv] **Towards Physically Executable 3D Gaussian for Embodied Navigation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SAGE-3D%3A+Towards+Physically+Executable+3D+Gaussian+for+Embodied+Navigation&btnG=) [[code]](https://github.com/Galery23/SAGE-3D_Official)
10. [2025 arXiv] **GaussianVision: Vision-Language Alignment from Compressed Image Representations using 2D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GaussianVision%3A+Vision-Language+Alignment+from+Compressed+Image+Representations+using+2D+Gaussian+Splatting&btnG=)
11. [2026 arXiv] **Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions** [[paper]](https://arxiv.org/abs/2602.04251)
12. [2025 ICCV] **RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=RegGS%3A+Unposed+Sparse+Views+Gaussian+Splatting+with+3DGS+Registration&btnG=) [[code]](https://github.com/3DAgentWorld/RegGS)
13. [2025 arXiv] **Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Smol-GS%3A+Compact+Representations+for+Abstract+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/AaltoML/Smol-GS)
14. [2026 ICRL] **YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=YoNoSplat%3A+You+Only+Need+One+Model+for+Feedforward+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/cvg/yonosplat)
15. [2026 arXiv] **Fast Converging 3D Gaussian Splatting for 1-Minute Reconstruction** [[paper]](https://scholar.google.com.hk/scholar?hl=zh-CN&as_sdt=0%2C5&q=Fast+Converging+3D+Gaussian+Splatting+for+1-Minute+Reconstruction&btnG=) [[code]](https://github.com/will-zzy/siggraph_asia)

<a name="Scene_Understanding"></a>  
## Scene Understanding

<a name="Per_Scene_Optimization_based_Methods"></a>  
### (i) Per-Scene Optimization-based Methods
1. [2024 CVPR] **LEGaussians: Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LEGaussians%3A+Language+Embedded+3D+Gaussians+for+Open-Vocabulary+Scene+Understanding&btnG=) [[code]](https://github.com/buaavrcg/LEGaussians)
2. [2024 CVPR] **Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Feature+3DGS%3A+Supercharging+3D+Gaussian+Splatting+to+Enable+Distilled+Feature+Fields&btnG=) [[code]](https://github.com/ShijieZhou-UCLA/feature-3dgs)
3. [2024 CVPR] **LangSplat: 3D Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LangSplat%3A+3D+Language+Gaussian+Splatting&btnG=) [[code]](https://github.com/minghanqin/LangSplat)
4. [2024 IJCV] **FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FMGS%3A+Foundation+Model+Embedded+3D+Gaussian+Splatting+for+Holistic+3D+Scene+Understanding&btnG=) [[code]](https://github.com/google-research/foundation-model-embedded-3dgs)
5. [2024 NeurIPS] **OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OpenGaussian%3A+Towards+Point-Level+3D+Gaussian-based+Open+Vocabulary+Understanding&btnG=) [[code]](https://github.com/yanmin-wu/OpenGaussian)
6. [2024 CVPR] **GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GaussianEditor%3A+Swift+and+Controllable+3D+Editing+with+Gaussian+Splatting&btnG=) [[code]](https://github.com/buaacyw/GaussianEditor)
7. [2024 CVPR] **OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OmniSeg3D%3A+Omniversal+3D+Segmentation+via+Hierarchical+Contrastive+Learning&btnG=) [[code]](https://github.com/OceanYing/OmniSeg3D-GS)
8. [2024 arXiv] **SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SuperGSeg%3A+Open-Vocabulary+3D+Segmentation+with+Structured+Super-Gaussians&btnG=)
9. [2025 ICCV] **CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=CLIP-GS%3A+Unifying+Vision-Language+Representation+with+3D+Gaussian+Splatting&btnG=)
10. [2024 arXiv] **LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LangSurf%3A+Language-Embedded+Surface+Gaussians+for+3D+Scene+Understanding&btnG=) [[code]](https://github.com/lifuguan/LangSurf)
11. [2025 AAAI] **Segment Any 3D Gaussians** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Segment+Any+3D+Gaussians&btnG=) [[code]](https://github.com/Jumpat/SegAnyGAussians)
12. [2025 CVPR] **4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=4D+LangSplat%3A+4D+Language+Gaussian+Splatting+via+Multimodal+Large+Language+Models&btnG=) [[code]](https://github.com/zrporz/4DLangSplat)
13. [2025 CVPR] **Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Rethinking+End-to-End+2D+to+3D+Scene+Segmentation+in+Gaussian+Splatting&btnG=) [[code]](https://github.com/Runsong123/Unified-Lift)
14. [2025 CVPR] **PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PanoGS%3A+Gaussian-based+Panoptic+Segmentation+for+3D+Open+Vocabulary+Scene+Understanding&btnG=) [[code]](https://github.com/zhaihongjia/PanoGS)
15. [2025 CVPR] **InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=InstanceGaussian%3A+Appearance-Semantic+Joint+Gaussian+Representation+for+3D+Instance-Level+Perception&btnG=) [[code]](https://github.com/lhj-git/InstanceGasuusian_code)
16. [2025 CVPR] **Efficient Decoupled Feature 3D Gaussian Splatting via Hierarchical Compression** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Efficient+Decoupled+Feature+3D+Gaussian+Splatting+via+Hierarchical+Compression&btnG=) [[code]](https://github.com/dai647/DF_3DGS)
17. [2025 ICCV] **CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=CCL-LGS%3A+Contrastive+Codebook+Learning+for+3D+Language+Gaussian+Splatting&btnG=)
18. [2025 ICCV] **CF3: Compact and Fast 3D Feature Fields** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=CF3%3A+Compact+and+Fast+3D+Feature+Fields&btnG=) [[code]](https://github.com/SNU-VGILab/CF3)
19. [2025 ICCV] **Identity-aware Language Gaussian Splatting for Open-vocabulary 3D Semantic Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Identity-aware+Language+Gaussian+Splatting+for+Open-vocabulary+3D+Semantic+Segmentation&btnG=) [[code]](https://github.com/DCVL-3D/ILGS_release)
20. [2025 ICCV] **SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SceneSplat%3A+Gaussian+Splatting-based+Scene+Understanding+with+Vision-Language+Pretraining&btnG=) [[code]](https://github.com/unique1i/SceneSplat)
21. [2025 NeurIPS] **SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SceneSplat%2B%2B%3A+A+Large+Dataset+and+Comprehensive+Benchmark+for+Language+Gaussian+Splatting&btnG=) [[code]](https://github.com/unique1i/SceneSplat_Benchmark)
22. [2025 NeurIPS] **LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LangSplatV2%3A+High-dimensional+3D+Language+Gaussian+Splatting+with+450%2B+FPS&btnG=) [[code]](https://github.com/ZhaoYujie2002/LangSplatV2)
23. [2025 ICCV] **LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LangScene-X%3A+Reconstruct+Generalizable+3D+Language-Embedded+Scenes+with+TriMap+Video+Diffusion&btnG=) [[code]](https://github.com/liuff19/LangScene-X)
24. [2025 NeurIPS] **COS3D: Collaborative Open-Vocabulary 3D Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=COS3D%3A+Collaborative+Open-Vocabulary+3D+Segmentation&btnG=) [[code]](https://github.com/Runsong123/COS3D)
25. [2025 NeurIPS] **PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PlanarGS%3A+High-Fidelity+Indoor+3D+Gaussian+Splatting+Guided+by+Vision-Language+Planar+Priors&btnG=) [[code]](https://github.com/SJTU-ViSYS-team/PlanarGS)
26. [2025 IROS] **OpenGS-Fusion: Open-Vocabulary Dense Mapping with Hybrid 3D Gaussian Splatting for Refined Object-Level Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OpenGS-Fusion%3A+Open-Vocabulary+Dense+Mapping+with+Hybrid+3D+Gaussian+Splatting+for+Refined+Object-Level+Understanding&btnG=) [[code]](https://github.com/YOUNG-bit/OpenGS-Fusion)
27. [2025 TOMM] **CLIP-GS: CLIP-Informed Gaussian Splatting for View-Consistent 3D Indoor Semantic Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=CLIP-GS%3A+CLIP-Informed+Gaussian+Splatting+for+View-Consistent+3D+Indoor+Semantic+Understanding&btnG=)
28. [2025 arXiv] **CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=CoRe-GS%3A+Coarse-to-Refined+Gaussian+Splatting+with+Semantic+Object+Focus&btnG=)
29. [2025 AAAI] **Bootstrapping Clustering of Gaussians for View-consistent 3D Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Bootstrapping+Clustering+of+Gaussians+for+View-consistent+3D+Scene+Understanding&btnG=) [[code]](https://github.com/wb014/FreeGS)
30. [2025 arXiv] **SemanticSplat: Feed-Forward 3D Scene Understanding with Language-Aware Gaussian Fields** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SemanticSplat%3A+Feed-Forward+3D+Scene+Understanding+with+Language-Aware+Gaussian+Fields&btnG=)
31. [2025 arXiv] **Uni3R: Unified 3D Reconstruction and Semantic Understanding via Generalizable Gaussian Splatting from Unposed Multi-View Images** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Uni3R%3A+Unified+3D+Reconstruction+and+Semantic+Understanding+via+Generalizable+Gaussian+Splatting+from+Unposed+Multi-View+Images&btnG=) [[code]](https://github.com/HorizonRobotics/Uni3R)
32. [2025 arXiv] **IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=IGGT%3A+Instance-Grounded+Geometry+Transformer+for+Semantic+3D+Reconstruction&btnG=) [[code]](https://github.com/lifuguan/IGGT_official)
33. [2025 NeurIPS] **FHGS: Feature-Homogenized Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FHGS%3A+Feature-Homogenized+Gaussian+Splatting&btnG=) [[code]](https://fhgs.cuastro.org/)
34. [2025 arXiv] **EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=EgoSplat%3A+Open-Vocabulary+Egocentric+Scene+Understanding+with+Language+Embedded+3D+Gaussian+Splatting&btnG=)
35. [2025 ICML] **ReferSplat: Referring Segmentation in 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=ReferSplat%3A+Referring+Segmentation+in+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/heshuting555/ReferSplat)
36. [2025 ACM MM] **GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GOI%3A+Find+3D+Gaussians+of+Interest+with+an+Optimizable+Open-vocabulary+Semantic-space+Hyperplane&btnG=) [[code]](https://github.com/Quyans/GOI-Hyperplane)
37. [2025 ACM MM] **SLGaussian: Fast Language Gaussian Splatting in Sparse Views** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SLGaussian%3A+Fast+Language+Gaussian+Splatting+in+Sparse+Views&btnG=) [[code]](https://github.com/chenkangjie1123/SLGaussian)
38. [2025 IMAVIS] **CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=CAGS%3A+Open-Vocabulary+3D+Scene+Understanding+with+Context-Aware+Gaussian+Splatting&btnG=) [[code]](https://github.com/Wistzz/CAGS)
39. [2025 ICLR] **HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=HiSplat%3A+Hierarchical+3D+Gaussian+Splatting+for+Generalizable+Sparse-View+Reconstruction&btnG=) [[code]](https://github.com/Open3DVLab/HiSplat)
40. [2025 arXiv] **Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Contrastive+Gaussian+Clustering%3A+Weakly+Supervised+3D+Scene+Segmentation&btnG=) [[code]](https://github.com/MyrnaCCS/contrastive-gaussian-clustering)
41. [2025 CVPR] **OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OpenSplat3D%3A+Open-Vocabulary+3D+Instance+Segmentation+using+Gaussian+Splatting&btnG=)
42. [2025 ICCV] **VoteSplat- Hough Voting Gaussian Splatting for 3D Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=VoteSplat-+Hough+Voting+Gaussian+Splatting+for+3D+Scene+Understanding&btnG=)
43. [2025 arXiv] **ShelfGaussian: Shelf-Supervised Open-Vocabulary Gaussian-based 3D Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=ShelfGaussian%3A+Shelf-Supervised+Open-Vocabulary+Gaussian-based+3D+Scene+Understanding&btnG=) [[code]](https://lunarlab-gatech.github.io/ShelfGaussian/)
44. [2025 arXiv] **C3G: Learning Compact 3D Representations with 2K Gaussians** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=C3G%3A+Learning+Compact+3D+Representations+with+2K+Gaussians&btnG=) [[code]](https://github.com/cvlab-kaist/C3G)
45. [2025 CVPRW] **DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=DINO+in+the+Room%3A+Leveraging+2D+Foundation+Models+for+3D+Segmentation&btnG=) [[code]](https://github.com/VisualComputingInstitute/DITR)
46. [2025 NeurIPS] **HouseLayout3D: A Benchmark and Training-Free Baseline for 3D Layout Estimation in the Wild** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=HouseLayout3D%3A+A+Benchmark+and+Training-Free+Baseline+for+3D+Layout+Estimation+in+the+Wild&btnG=) [[code]](https://github.com/HouseLayout3D/houselayout3d)
47. [2025 CVPRW] **Segment Any Primitive-Zero-Shot 3D Primitive Segmentation from Point Cloud** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Segment+Any+Primitive-Zero-Shot+3D+Primitive+Segmentation+from+Point+Cloud&btnG=)
48. [2024 CVPR] **MaskClustering- View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MaskClustering-+View+Consensus+based+Mask+Graph+Clustering+for+Open-Vocabulary+3D+Instance+Segmentation&btnG=) [[code]](https://github.com/PKU-EPIC/MaskClustering)
49. [2026 ICLR] **FineSplat: Fine-Grained 3D Open-Vocabulary Language Gaussian Splatting** [[paper]](https://openreview.net/pdf?id=y72GwPF8YO)
50. [2026 ICLR] **QuantileRender: Efficiently Embedding High-Dimensional Features on 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=QuantileRender%3A+Efficiently+Embedding+High-Dimensional+Features+on+3D+Gaussian+Splatting&btnG=)
51. [2025 ICML] **Tackling View-Dependent Semantics in 3D Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Tackling+View-Dependent+Semantics+in+3D+Language+Gaussian+Splatting&btnG=) [[code]](https://github.com/SJTU-DeepVisionLab/LaGa)
52. [2025 arXiv] **FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FLEG%3A+Feed-Forward+Language+Embedded+Gaussian+Splatting+from+Any+Views&btnG=)
53. [2025 ACM TOG] **AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=2025-ACM-AnySplat%3A+Feed-forward+3D+Gaussian+Splatting+from+Unconstrained+Views&btnG=) [[code]](https://github.com/InternRobotics/AnySplat)
54. [2025 arXiv] **Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Self-Supervised+and+Generalizable+Tokenization+for+CLIP-Based+3D+Understanding&btnG=)
55. [2025 arXiv] **POMA-3D: The Point Map Way to 3D Scene Understanding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=POMA-3D%3A+The+Point+Map+Way+to+3D+Scene+Understanding&btnG=)
56. [2025 arXiv] **Quantile Rendering: Efficiently Embedding High-dimensional Feature on 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Quantile+Rendering%3A+Efficiently+Embedding+High-dimensional+Feature+on+3D+Gaussian+Splatting&btnG=)
57. [2025 arXiv] **Monet: Reasoning in Latent Visual Space Beyond Images and Language** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Monet%3A+Reasoning+in+Latent+Visual+Space+Beyond+Images+and+Language&btnG=) [[code]](https://github.com/NOVAglow646/Monet)
58. [2025 arXiv] **UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=UniC-Lift%3A+Unified+3D+Instance+Segmentation+via+Contrastive+Learning&btnG=) [[code]](https://github.com/val-iisc/UniC-Lift)
59. [2026 arXiv] **FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FeatureSLAM%3A+Feature-enriched+3D+gaussian+splatting+SLAM+in+real+time&btnG=)
60. [2025 ICCV] **Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Joint+Semantic+and+Rendering+Enhancements+in+3D+Gaussian+Modeling+with+Anisotropic+Local+Encoding&btnG=) [[code]](https://github.com/fly519/JSRE)
61. [2025 ICCV] **Online Language Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Online+Language+Splatting&btnG=) [[code]](https://github.com/rpng/online_lang_splatting)
62. [2025 ICRL] **UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=UniGS%3A+Unified+Language-Image-3D+Pretraining+with+Gaussian+Splatting&btnG=) [[code]](https://github.com/Li-Hao-yuan/UniGS)
63. [2025 arXiv] **Hi-lsplat: Hierarchical 3d language gaussian splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Hi-LSplat%3A+Hierarchical+3D+Language+Gaussian+Splatting&btnG=)
64. [2025 arXiv] **UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PointGauss%3A+Point+Cloud-Guided+Multi-Object+Segmentation+for+Gaussian+Splatting&btnG=) [[code]](https://github.com/hbycswt/pointgauss)
65. [2025 ICCV] **No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=No+Pose+at+All%3A+Self-Supervised+Pose-Free+3D+Gaussian+Splatting+from+Sparse+Views&btnG=) [[code]](https://github.com/ranrhuang/SPFSplat)
66. [2025 arXiv] **econsg: Efficient and multi-view consistent open-vocabulary 3d semantic gaussians** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=econSG%3A+Efficient+and+Multi-view+Consistent+Open-Vocabulary+3D+Semantic+Gaussians&btnG=) [[code]](https://github.com/lulusindazc/econSG)
67. [2025 CVPR] **Reasongrounder: Lvlm-guided hierarchical feature splatting for open-vocabulary 3d visual grounding and reasoning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=ReasonGrounder%3A+LVLM-Guided+Hierarchical+Feature+Splatting+for+Open-Vocabulary+3D+Visual+Grounding+and+Reasoning&btnG=) [[code]](https://github.com/ZhenyangLiu/ReasonGrounder)
68. [2025 arXiv] **GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GALA%3A+Guided+Attention+with+Language+Alignment+for+Open+Vocabulary+Gaussian+Splatting&btnG=)
69. [2025 arXiv] **SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SEGSPLAT%3A+FEED-FORWARD+GAUSSIAN+SPLATTING+AND+OPEN-SET+SEMANTIC+SEGMENTATION&btnG=)
70. [2025 ACM] **Textsplat: Text-guided semantic fusion for generalizable gaussian splatting** [[paper]](https://scholar.google.com.hk/scholar?hl=zh-CN&as_sdt=0%2C5&q=extSplat%3A+Text-Guided+Semantic+Fusion+for+Generalizable+Gaussian+Splatting&btnG=)
71. [2025 ICCV] **Openinsgaussian: Open-vocabulary instance gaussian segmentation with context-aware cross-view fusion** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=OpenInsGaussian%3A+Open-vocabulary+Instance+Gaussian+Segmentation+with+Context-aware+Cross-view+Fusion&btnG=) [[code]](https://github.com/TianyuHuang-000/OpenInsGaussian)
72. [2026 arXiv] **ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=ProFuse%3A+Efficient+Cross-View+Context+Fusion+for+Open-Vocabulary+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/chiou1203/ProFuse)
73. [2025 ICCV] **Spatialsplat: Efficient semantic 3d from sparse unposed images** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SpatialSplat%3A+Efficient+Semantic+3D+from+Sparse+Unposed+Images&btnG=) [[code]](https://github.com/shengyuuu/SpatialSplat)
74. [2025 arXiv] **UniForward: Unified 3D Scene and Semantic Field Reconstruction via Feed-Forward Gaussian Splatting from Only Sparse-View Images** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=UniForward%3A+Unified+3D+Scene+and+Semantic+Field+Reconstruction+via+Feed-Forward+Gaussian+Splatting+from+Only+Sparse-View+Images&btnG=)
75. [2026 arXiv] **Split&Splat: Zero-Shot Panoptic Segmentation via Explicit Instance Modeling and 3D Gaussian Splatting** [[paper]](https://scholar.google.com.hk/scholar?hl=zh-CN&as_sdt=0%2C5&q=Split%26Splat%3A+Zero-Shot+Panoptic+Segmentation+via+Explicit+Instance+Modeling+and+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/LTTM/Split_and_Splat)
76. [2025 ICCV] **Objectgs: Object-aware scene reconstruction and scene understanding via gaussian splatting** [[paper]](https://scholar.google.com.hk/scholar?hl=zh-CN&as_sdt=0%2C5&q=ObjectGS%3A+Object-aware+Scene+Reconstruction+and+Scene+Understanding+via+Gaussian+Splatting&btnG=) [[code]](https://github.com/RuijieZhu94/ObjectGS)
77. [2025 ICLR] **3D Vision-Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=3D+VISION-LANGUAGE+GAUSSIAN+SPLATTING&btnG=)
78. [2025 NeurIPS] **Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Segment+then+Splat%3A+Unified+3D+Open-Vocabulary+Segmentation+via+Gaussian+Splatting&btnG=) [[code]](https://github.com/vulab-AI/Segment-then-Splat)
79. [2026 arXiv] **GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=GS-CLIP%3A+Zero-shot+3D+Anomaly+Detection+by+Geometry-Aware+Prompt+and+Synergistic+View+Representation+Learning&btnG=) [[code]](https://github.com/zhushengxinyue/GS-CLIP)

<a name="Per_Scene_Optimization_Free_Methods"></a>  
### (ii) Per-Scene Optimization-Free Methods
1. [2024 arXiv] **Occam's LGS: An Efficient Approach for Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Occam%27s+LGS%3A+An+Efficient+Approach+for+Language+Gaussian+Splatting&btnG=) [[code]](https://github.com/insait-institute/OccamLGS)
2. [2025 arXiv] **Visibility-Aware Language Aggregation for Open-Vocabulary Segmentation in 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Visibility-Aware+Language+Aggregation+for+Open-Vocabulary+Segmentation+in+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/changandao/VALA)
3. [2025 ICCV] **LUDVIG: Learning-free Uplifting of 2D Visual Features to Gaussian Splatting Scenes** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LUDVIG%3A+Learning-free+Uplifting+of+2D+Visual+Features+to+Gaussian+Splatting+Scenes&btnG=) [[code]](https://github.com/naver/ludvig)
4. [2025 WACV] **Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Lifting+by+Gaussians%3A+A+Simple%2C+Fast+and+Flexible+Method+for+3D+Instance+Segmentation&btnG=)
5. [2025 ACM MM] **Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Training-Free+Hierarchical+Scene+Understanding+for+Gaussian+Splatting+with+Superpoint+Graphs&btnG=) [[code]](https://github.com/Atrovast/THGS)
6. [2025 CVPR] **Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Dr.+Splat%3A+Directly+Referring+3D+Gaussian+Splatting+via+Direct+Language+Embedding+Registration&btnG=) [[code]](https://github.com/kaist-ami/Dr-Splat)
7. [2025 arXiv] **Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Chorus%3A+Multi-Teacher+Pretraining+for+Holistic+3D+Gaussian+Scene+Encoding&btnG=)
8. [2025 ICCV] **Trace3d: Consistent segmentation lifting via gaussian instance tracing** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Trace3D%3A+Consistent+Segmentation+Lifting+via+Gaussian+Instance+Tracing&btnG=) [[code]](https://github.com/trace-3d/Trace3D)
9. [2025 arXiv] **Polysemous Language Gaussian Splatting via Matching-based Mask Lifting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Polysemous+Language+Gaussian+Splatting+via+Matching-based+Mask+Lifting&btnG=)

<a name="NeurIPS_2025"></a>  
### NeurIPS 2025
1. [2024 10.18] **Large Spatial Model: End-to-end Unposed Images to Semantic 3D** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Large+Spatial+Model%3A+End-to-end+Unposed+Images+to+Semantic+3D&btnG=) [[code]](https://github.com/NVlabs/LSM)
2. [2025 03.29] **Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Segment+then+Splat%3A+Unified+3D+Open-Vocabulary+Segmentation+via+Gaussian+Splatting&btnG=)
3. [2025 05.19] **FHGS: Feature-Homogenized Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FHGS%3A+Feature-Homogenized+Gaussian+Splatting&btnG=)
4. [2025 06.08] **SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SceneSplat%2B%2B%3A+A+Large+Dataset+and+Comprehensive+++Benchmark+for+Language+Gaussian+Splatting&btnG=) [[code]](https://github.com/unique1i/SceneSplat_Benchmark)
5. [2025 07.02] **SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SIU3R%3A+Simultaneous+Scene+Understanding+and+3D+Reconstruction+Beyond+Feature+Alignment&btnG=) [[code]](https://github.com/WU-CVGL/SIU3R)
6. [2025 07.07] **LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=LangSplatV2%3A+High-dimensional+3D+Language+Gaussian+Splatting+with+450%2B+FPS&btnG=) [[code]](https://github.com/ZhaoYujie2002/LangSplatV2)
7. [2025 10.23] **Concerto- Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Concerto-+Joint+2D-3D+Self-Supervised+Learning+Emerges+Spatial+Representations&btnG=) [[code]](https://github.com/Pointcept/Concerto)
8. [2025 10.23] **PlanarGS: High-Fidelity Indoor 3D Gaussian Splatting Guided by Vision-Language Planar Priors** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PlanarGS%3A+High-Fidelity+Indoor+3D+Gaussian+Splatting+Guided+by+Vision-Language+Planar+Priors&btnG=) [[code]](https://github.com/SJTU-ViSYS-team/PlanarGS)
9. [2025 12.02] **HouseLayout3D: A Benchmark and Training-Free Baseline for 3D Layout Estimation in the Wild** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=HouseLayout3D%3A+A+Benchmark+and+Training-Free+Baseline+for+3D+Layout+Estimation+in+the+Wild&btnG=) [[code]](https://github.com/HouseLayout3D/houselayout3d)
10. [2025 05.19] **FHGS: Feature-Homogenized Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FHGS%3A+Feature-Homogenized+Gaussian+Splatting&btnG=) [[code]](https://fhgs.cuastro.org/)

<a name="ICLR_2026"></a>  
### ICLR 2026
1. [2026 09.15] **FineSplat: Fine-Grained 3D Open-Vocabulary Language Gaussian Splatting** [[paper]](https://openreview.net/pdf?id=y72GwPF8YO)
2. [2026 09.16] **QuantileRender: Efficiently Embedding High-Dimensional Features on 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=QuantileRender%3A+Efficiently+Embedding+High-Dimensional+Features+on+3D+Gaussian+Splatting&btnG=)
3. [2025 10.08] **TIGaussian: Disentangle Gaussians for Spatial-Awared Text-Image-3D Alignment** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=TIGaussian%3A+Disentangle+Gaussians+for+Spatial-Awared+Text-Image-3D+Alignment&btnG=)
4. [2025 06.06] **HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=HiSplat%3A+Hierarchical+3D+Gaussian+Splatting+for+Generalizable+Sparse-View+Reconstruction&btnG=) [[code]](https://github.com/Open3DVLab/HiSplat)
5. [2026 ICRL] **YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=YoNoSplat%3A+You+Only+Need+One+Model+for+Feedforward+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/cvg/yonosplat)

<a name="Point_cloud_processing"></a>  
### Point cloud processing
1. [2025 CVPR] **All in One: Visual-Description-Guided Unified Point Cloud Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=All+in+One%3A+Visual-Description-Guided+Unified+Point+Cloud+Segmentation&btnG=) [[code]](https://github.com/Hanzy1996/VDG-Uni3DSeg)
2. [2026 arxiv] **MVGGT: Multimodal Visual Geometry Grounded Transformer for Multiview 3D Referring Expression Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=MVGGT%3A+Multimodal+Visual+Geometry+Grounded+Transformer+for+Multiview+3D+Referring+Expression+Segmentation&btnG=) [[code]](https://github.com/sosppxo/mvggt)
3. [2026 arxiv] **3D CoCa v2: Contrastive Learners with Test-Time Search for Generalizable Spatial Intelligence** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=3D+CoCa+v2%3A+Contrastive+Learners+with+Test-Time+Search+for+Generalizable+Spatial+Intelligence&btnG=) [[code]](https://github.com/AIGeeksGroup/3DCoCav2.)
4. [2025 NeurlPS] **SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentation** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=SegGraph%3A+Leveraging+Graphs+of+SAM+Segments+for+Few-Shot+3D+Part+Segmentation&btnG=) [[code]](https://github.com/YueyangHu-2000/SegGraph)
5. [2025 Plant Communications] **A novel point cloud completion model for three-dimensional reconstruction of complex, dynamic population-level crop canopy architecture** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=A+novel+point+cloud+completion+model+for+three-dimensional+reconstruction+of+complex%2C+dynamic+population-level+crop+canopy+architecture&btnG=)

<a name="3D_Fusion"></a>  
### 3D Fusion
1. [2025 arxiv] **Automated 3D-GS Registration and Fusion via Skeleton Alignment and Gaussian-Adaptive Features** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Automated+3D-GS+Registration+and+Fusion+via+Skeleton+Alignment+and+Gaussian-Adaptive+Features&btnG=)
2. [2025 SIGGRAPH] **EGG-Fusion: Efficient 3D Reconstruction with Geometry-aware Gaussian Surfel on the Fly** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=EGG-Fusion-+Efficient+3D+Reconstruction+with+Geometry-aware+Gaussian+Surfel+on+the+Fly&btnG=) [[code]](https://github.com/panxkun/eggfusion)
3. [2025 arxiv] **FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=FaithFusion-+Harmonizing+Reconstruction+and+Generation+via+Pixel-wise+Information+Gain&btnG=) [[code]](https://github.com/wangyuanbiubiubiu/FaithFusion)
4. [2025 arxiv] **PFGS: Pose-Fused 3D Gaussian Splatting for Complete Multi-Pose Object Reconstruction** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=PFGS-+Pose-Fused+3D+Gaussian+Splatting+for+Complete+Multi-Pose+Object+Reconstruction&btnG=)
5. [2026 arxiv] **ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=ProFuse-+Efficient+Cross-View+Context+Fusion+for+Open-Vocabulary+3D+Gaussian+Splatting&btnG=) [[code]](https://github.com/chiou1203/ProFuse)
6. [2026 arxiv] **ShapeR: Robust Conditional 3D Shape Generation from Casual Captures** [[paper]](https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=ShapeR%3A+Robust+Conditional+3D+Shape+Generation+from+Casual+Captures&btnG=) [[code]](https://github.com/facebookresearch/ShapeR)

-----------------------------------------------------------------------------------------------

<a name="Remote_Sensing"></a>  
## Remote Sensing
1. [2025 arXiv] **DynamicEarth: How Far are We from Open-Vocabulary Change Detection?** [[paper]](https://arXiv.org/abs/2501.12931) [[code]](https://github.com/likyoo/DynamicEarth)
2. [2025 ICCV] **SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation.** [[paper]](https://arXiv.org/abs/2507.12857) [[code]](https://github.com/HuangShiqi128/SCORE)
4. [2026 AAAI] **RSKT-Seg: Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing** [[paper]](https://arxiv.org/pdf/2509.12040) [[code]](https://github.com/LiBingyu01/RSKT-Seg)
5. [2025 Arxiv] **AlignCLIP: Self-Guided Alignment for Remote Sensing Open-Vocabulary Semantic Segmentation** [[paper]](https://openreview.net/forum?id=hpD3tn7Xbp) [[code]](https://openreview.net/attachment?id=hpD3tn7Xbp&name=supplementary_material)
6. [2026 AAAI] **RSVG-ZeroOV: Exploring a Training-Free Framework for Zero-Shot Open-Vocabulary Visual Grounding in Remote Sensing Images** [[paper]](https://arxiv.org/pdf/2509.18711?) [[code]](https://github.com/like413/RSVG-ZeorOV)
8. [2025 AAAI] **GSNet: Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation** [[paper]](https://arxiv.org/abs/2412.19492) [[code]](https://github.com/yecy749/GSNet)
9. [2025 CVPRW] **AerOSeg: Harnessing SAM for Open-Vocabulary Segmentation in Remote Sensing Images** [[paper]](https://arxiv.org/abs/2412.19492)
10. [2025 CVPR] **SegEarthOV-1: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images.** [[paper]](https://arXiv.org/abs/2410.01768) [[code]](https://github.com/likyoo/SegEarth-OV)
11. [2025 Arxiv] **SegEarthOV-2: Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images** [[paper]](https://arxiv.org/abs/2508.18067)  [[code]](https://github.com/earth-insights/SegEarth-OV-2)
12. [2025 Arxiv] **SegEarthOV-3: SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images** [[paper]](https://arxiv.org/pdf/2512.08730)  [[code]](https://github.com/earth-insights/SegEarth-OV-3)

***************************************************************
1. [2025 TGRS] **A Unified Framework With Multimodal Fine-Tuning for Remote Sensing Semantic Segmentation.** [[paper]](https://ieeexplore.ieee.org/document/11063320) [[code]](https://github.com/sstary/SSRS)
3. [2025 ICASSP] **Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification.** [[paper]](https://arXiv.org/abs/2409.00698) [[code]](https://github.com/elkhouryk/RS-TransCLIP)
5. [2025 ICCV] **Dynamic Dictionary Learning for Remote Sensing Image Segmentation.** [[paper]](https://arXiv.org/pdf/2503.06683) [[code]](https://github.com/XavierJiezou/D2LS)
6. [2025 ICCV] **GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks.** [[paper]](https://arxiv.org/pdf/2411.19325) [[code]](https://github.com/The-AI-Alliance/GEO-Bench-VLM)
8. [2025 ICCV] **When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning.** [[paper]](https://arXiv.org/pdf/2503.07588) [[code]](https://github.com/VisionXLab/LRS-VQA)
9. [2025 AAAI] **ZoRI: Towards discriminative zero-shot remote sensing instance segmentation.** [[paper]](https://arXiv.org/abs/2412.12798) [[code]](https://github.com/HuangShiqi128/ZoRI)
10. [2024 NIPS] **Segment Any Change.** [[paper]](https://proceedings.NIPS.cc/paper_files/paper/2024/file/9415416201aa201902d1743c7e65787b-Paper-Conference.pdf) [[code]](https://github.com/Z-Zheng/pytorch-change-models)
12. [2025 CVPR] **XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?** [[paper]](https://arXiv.org/abs/2503.23771) [[code]](https://github.com/EvolvingLMMs-Lab/XLRS-Bench)
13. [2025 CVPR] **Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Exact_Exploring_Space-Time_Perceptive_Clues_for_Weakly_Supervised_Satellite_Image_CVPR_2025_paper.pdf) [[code]](https://github.com/MiSsU-HH/Exact)
16. [2025 Arxiv] **InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition** [[paper]](https://arxiv.org/pdf/2505.15818) [[code]](https://github.com/VoyagerXvoyagerx/InstructSAM)
17. [2025 Arxiv] **DescribeEarth: Describe Anything for Remote Sensing Images** [[paper]](https://arxiv.org/pdf/2509.25654v1) [[code]](https://github.com/earth-insights/DescribeEarth)
18. [2025 NIPS] **GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset** [[paper]](https://arxiv.org/abs/2507.14697) [[code]](https://github.com/Z-ZW-WXQ/GTPBD)
19. [2025 Arxiv] **RS3DBench: A Comprehensive Benchmark for 3D Spatial Perception in Remote Sensing** [[paper]](https://arxiv.org/abs/2509.18897) [[code]](https://rs3dbench.github.io)
20. [2025 Arxiv] **DGL-RSIS: Decoupling Global Spatial Context and Local Class Semantics for Training-Free Remote Sensing Image Segmentation** [[paper]](https://arxiv.org/pdf/2509.00598) [[code]](https://github.com/designer1024/DGL-RSIS)
21. [2025 TGRS] **A Unified SAM-Guided Self-Prompt Learning Framework for Infrared Small Target Detection** [[paper]](https://ieeexplore.ieee.org/document/11172325) [[code]](https://github.com/fuyimin96/SAM-SPL)
22. [2025 TGRS] **Semantic Prototyping With CLIP for Few-Shot Object Detection in Remote Sensing Images** [[paper]](https://ieeexplore.ieee.org/document/10930588)
23. [2025 Arxiv] **ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild** [[paper]](https://arxiv.org/pdf/2501.13354) [[code]](https://github.com/waterdisappear/ATRNet-STAR)
25. [2025 ISPRS]  **AdaptVFMs-RSCD: Advancing Remote Sensing Change Detection from binary to semantic with SAM and CLIP** [[paper]](https://doi.org/10.1016/j.isprsjprs.2025.09.010) [[data]](https://github.com/Jiang-CHD-YunNan/RS-VFMs-Fine-tuning-Dataset)
26. [2025 Arxiv] **PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection** [[paper]](https://arxiv.org/pdf/2509.09572) [[code]](https://github.com/dyzy41/PeftCD)
28. [2025 Arxiv] **Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models** [[paper]](https://arxiv.org/pdf/2510.07135) [[code]](https://github.com/elkhouryk/fewshot_RS)
29. [2025 RSE] **Strategic sampling for training a semantic segmentation model in operational mapping: Case studies on cropland parcel extraction** [[paper]](https://doi.org/10.1016/j.rse.2025.115034) [[data]](https://doi.org/10.5281/zenodo.16595511) [[code]](https://github.com/Remote-Sensing-of-Land-Resource-Lab/Training-Sample-Selection)
31. [2025 CVPR] **SkySense-O:TowardsOpen-WorldRemoteSensingInterpretation withVision-CentricVisual-LanguageModeling** [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SkySense-O_Towards_Open-World_Remote_Sensing_Interpretation_with_Vision-Centric_Visual-Language_Modeling_CVPR_2025_paper.pdf) [[code]](https://github.com/zqcrafts/SkySense-O)
32. [2025 Arxiv] **SAR-KnowLIP: Towards Multimodal Foundation Models for Remote Sensing** [[paper]](https://arxiv.org/pdf/2509.23927) [[code]](https://github.com/yangyifremad/SARKnowLIP)
33. [2025 Arxiv] **LG-CD: Enhancing Language-Guided Change Detection through SAM2 Adaptation** [[paper]](https://arxiv.org/pdf/2509.21894v1)
34. [2025 CVM] **Remote sensing tuning: A survey** [[paper]](https://ieeexplore.ieee.org/document/11119145) [[code]](https://github.com/DongshuoYin/Remote-Sensing-Tuning-A-Survey)
35. [2025 ISPRS] **Identifying rural roads in remote sensing imagery: From benchmark dataset to coarse-to-fine extraction network—A case study in China** [[paper]](https://doi.org/10.1016/j.isprsjprs.2025.11.010) [[data]](https://rsidea.whu.edu.cn/resource_sharing.htm)
36. [2025 NatureMI] **A semantic-enhanced multi-modal remote sensing foundation model for Earth observation** [[paper]](https://www.nature.com/articles/s42256-025-01078-8)
37. [2025 NIPS] **Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind** [[paper]](https://arxiv.org/pdf/2505.12207) [[code]](https://github.com/rssysu/AgroMind)
38. [2025 TPAMI] **RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning** [[paper]](https://arxiv.org/pdf/2409.13366)
39. [2025 Arxiv] **FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection** [[paper]](https://arxiv.org/pdf/2509.15788) [[code]](https://github.com/zmoka-zht/FoBa)
40. [2025 TGRS] **Multimodal Visual-Language Prompt Network for Remote Sensing Few-Shot Segmentation** [[paper]](https://ieeexplore.ieee.org/abstract/document/11071646) [[code]](https://github.com/Gritiii/MVLPNet)
41. [2026 AAAI] **RIS-LAD: A Benchmark and Model for Referring Low-Altitude Drone Image Segmentation** [[paper]](https://arxiv.org/pdf/2507.20920) [[code]](https://github.com/AHideoKuzeA/RIS-LAD-A-Benchmark-and-Model-for-Referring-Low-Altitude-Drone-Image-Segmentation)

<a name="Training_Free"></a>
## Training Free Segmentation
### VLM Only
1. [2023 arXiv] **CLIPSurgery: CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks.** [[paper]](https://arXiv.org/pdf/2304.05653) [[code]](https://github.com/xmed-lab/CLIP_Surgery)
2. [2024 arXiv] **SC-CLIP: Self-Calibrated CLIP for Training-Free Open-Vocabulary Segmentation.** [[paper]](https://arXiv.org/pdf/2411.15869) [[code]](https://github.com/SuleBai/SC-CLIP)
3. [2025 arXiv] **A Survey on Training-free Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2505.22209)
4. [2022 ECCV] **Maskclip: Extract Free Dense Labels from CLIP.** [[paper]](https://arXiv.org/pdf/2112.01071) [[code]](https://github.com/chongzhou96/MaskCLIP)
5. [2024 ECCV] **SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.** [[paper]](https://arXiv.org/pdf/2312.01597) [[code]](https://github.com/wangf3014/SCLIP)
6. [2024 ECCV] **CLIPtrase: Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2407.08268) [[code]](https://github.com/leaves162/CLIPtrase)
7. [2024 ECCV] **ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference.** [[paper]](https://arXiv.org/pdf/2407.12442) [[code]](https://github.com/mc-lan/ClearCLIP)
8. [2025 AAAI] **Unveiling the Knowledge of CLIP for Training-Free Open-Vocabulary Semantic Segmentation.** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/32602) [[code]](https://ojs.aaai.org/index.php/AAAI/article/view/32602)
9. [2022 NIPS] **ReCo: Retrieve and Co-segment for Zero-shot Transfer.** [[paper]](https://arXiv.org/pdf/2206.07045) [[code]](https://github.com/NoelShin/reco)
10. [2024 WACV] **NACLIP: Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2404.08181) [[code]](https://github.com/sinahmr/NACLIP)
11. [2024 ICLR] **Vision Transformers Need Registers.** [[paper]](https://arXiv.org/pdf/2309.16588) [[code]](https://github.com/kyegomez/Vit-RGTS)
12. [2024 ICLR] **Vision Transformers Don't Need Trained Registers.** [[paper]](https://arXiv.org/pdf/2506.08010) [[code]](https://github.com/nickjiang2378/test-time-registers/tree/main)
13. [2025 arXiv] **Post-Training Quantization of Vision Encoders Needs Prefixing Registers** [[paper]](https://arxiv.org/pdf/2510.04547v1)
14. [2025 arXiv] **To sink or not to sink: visual information pathways in large vision-language models** [[paper]](https://arxiv.org/pdf/2510.08510v1)
15. [2025 CVPR] **ResCLIP: Residual Attention for Training-free Dense Vision-language Inference.** [[paper]](https://arXiv.org/pdf/2411.15851) [[code]](https://github.com/yvhangyang/ResCLIP)
16. [2024 CVPR] **GEM: Grounding Everything: Emerging Localization Properties in Vision-Language Transformers.** [[paper]](https://arXiv.org/pdf/2312.00878) [[code]](https://github.com/WalBouss/GEM)
17. [2025 CVPRW] **ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements.** [[paper]](https://arXiv.org/pdf/2411.12044) [[code]](https://github.com/m-arda-aydn/ITACLIP)
18. [2025 arXiv] **Improving visual discriminability of clip for training-free open-vocabulary semantic segmentation** [[paper]](https://openreview.net/attachment?id=tKO2l8oICt&name=pdf)
19. [2025 ICCV] **ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation.** [[paper]](https://arXiv.org/pdf/2506.21233) [[code]](https://github.com/xiweix/ReME)
20. [2024 CVPR] **CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor.** [[paper]](https://arXiv.org/pdf/2312.07661) [[code]](https://github.com/kevin-ssy/CLIP_as_RNN)

***************************************************************
### VLM & VFM & Diffusion & SAM
1. [2024 arXiv] **CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2411.13836) [[code]](https://github.com/linsun449/cliper.code)
2. [2024 ECCV] **ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation.** [[paper]](https://arXiv.org/pdf/2408.04883v1) [[code]](https://github.com/mc-lan/ProxyCLIP)
3. [2024 ECCV] **CLIP_Dinoiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation.** [[paper]](https://arXiv.org/pdf/2312.12359) [[code]](https://github.com/wysoczanska/clip_dinoiser)
4. [2024 WACV] **CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free.** [[paper]](https://arXiv.org/pdf/2309.14289) [[code]](https://github.com/wysoczanska/clip-diy)
5. [2024 IJCV] **IPSeg: Towards Training-free Open-world Segmentation via Image Prompting Foundation Models.** [[paper]](https://arXiv.org/pdf/2310.10912) [[code]](https://github.com/luckybird1994/IPSeg)
6. [2025 ICCV] **CorrCLIP: Reconstructing Correlations in CLIP with Off-the-Shelf Foundation Models for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2411.10086) [[code]](https://github.com/zdk258/CorrCLIP/tree/master)
7. [2025 ICCV] **Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation.** [[paper]](https://arXiv.org/pdf/2411.19331) [[code]](https://github.com/lorebianchi98/Talk2DINO)
8. [2025 CVPR] **dino.txt: DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment** [[paper]](https://arxiv.org/pdf/2412.16334) [[code]](https://github.com/facebookresearch/dinov2)
9. [2025 ICCV] **ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation.** [[paper]](https://arXiv.org/pdf/2506.21233) [[code]](https://github.com/xiweix/ReME)
10. [2025 ICCV] **FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2504.10487) [[code]](https://github.com/yasserben/FLOS
11. [2025 ICCV] **Trident: Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation.** [[paper]](https://arXiv.org/pdf/2411.09219) [[code]](https://github.com/YuHengsss/Trident)
12. [2024 NIPS] **DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut.** [[paper]](https://arXiv.org/pdf/2406.02842v2) [[code]](https://github.com/PaulCouairon/DiffCut)
13. [2025 NIPS] **TextRegion: Text-Aligned Region Tokens from Frozen Image-Text Models.** [[paper]](https://arXiv.org/pdf/2505.23769) [[code]](https://github.com/avaxiao/TextRegion)
14. [2024 AAAI] **TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training.** [[paper]](https://arXiv.org/pdf/2312.12828) [[code]](https://github.com/linyq2117/TagCLIP)
15. [2024 ICLR] **EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models.** [[paper]](https://arXiv.org/pdf/2401.11739) [[code]](https://github.com/linyq2117/TagCLIP)
16. [2024 ICML] **Language-driven Cross-modal Classifier for Zero-shot Multi-label Image Recognition.** [[paper]](https://openreview.net/pdf?id=sHswzNWUW2) [[code]](https://github.com/yic20/CoMC)
17. [2025 ICML] **FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification.** [[paper]](https://openreview.net/pdf?id=dewR2augg2)
18. [2025 ICML] **Multi-Modal Object Re-Identification via Sparse Mixture-of-Experts.** [[paper]](https://openreview.net/pdf?id=uvFE58mSnR) [[code]](https://github.com/stone96123/MFRNet)
19. [2024 CVPR] **FreeDA: Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation.** [[paper]](https://arXiv.org/pdf/2404.06542) [[code]](https://github.com/aimagelab/freeda)
20. [2024 ECCV] **Diffusion Models for Open-Vocabulary Segmentation** [[paper]](https://arxiv.org/pdf/2306.09316) [[code]](https://github.com/karazijal/ovdiff)
21. [2025 CVPR] **GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery.** [[paper]](https://arXiv.org/pdf/2403.09974) [[code]](https://github.com/enguangW/GET)
22. [2025 CVPR] **CCD: Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification.** [[paper]](https://arXiv.org/pdf/2503.16873) [[code]](https://github.com/k0u-id/CCD)
23. [2025 CVPR] **SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2502.16911?) [[code]](https://github.com/kjmillerCURIS/SPARC)
24. [2025 CVPR] **LOPSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2503.19777) [[code]](https://github.com/vladan-stojnic/LPOSS/tree/main)
25. [2025 arXiv] **One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework** [[paper]](https://arxiv.org/pdf/2510.02898v1) [[code]](https://paciosoft.com/Patch-ioner/)
26. [2025 CVPR] **CASS: Distilling Spectral Graph for Object-Context Aware Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2411.17150) [[code]](https://micv-yonsei.github.io/cass/)
27. [2025 ICML] **Unlocking the Power of SAM 2 for Few-Shot Segmentation** [[paper]](https://arxiv.org/pdf/2505.14100) [[code]](https://github.com/Sam1224/FSSAM)
28. [2025 arXiv] **Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation** [[paper]](https://arxiv.org/pdf/2510.19592v1) [[code]](https://github.com/HYUNJS/DecAF)
29. [2024 WACV] **FOSSIL: Free Open-Vocabulary Semantic Segmentation through Synthetic References Retrieval.** [[paper]](https://openaccess.thecvf.com/content/WACV2024/papers/Barsellotti_FOSSIL_Free_Open-Vocabulary_Semantic_Segmentation_Through_Synthetic_References_Retrieval_WACV_2024_paper.pdf)
30. [2024 NIPS] **Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts** [[paper]](https://arxiv.org/pdf/2410.05963)
31.

***************************************************************
1. [2024 AAAI] **TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training.** [[paper]](https://arXiv.org/pdf/2312.12828) [[code]](https://github.com/linyq2117/TagCLIP)
2. [2024 CVPR] **Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2311.17095) [[code]](https://github.com/letitiabanana/PnP-OVSS)
3. [2024 CVPR] **Image-to-Image Matching via Foundation Models: A New Perspective for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2404.00262) [[code]](https://github.com/072jiajia/image-text-co-decomposition)
4. [2024 ECCV] **In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2408.04961) [[code]](https://github.com/dahyun-kang/lavg)
5. [2025 ICCV] **LUDVIG: Learning-free uplifting of 2D visual features to gaussuan splatting scenes.** [[paper]](https://arXiv.org/pdf/2410.14462#page=17.85) [[code]](https://github.com/naver/ludvig)
6. [2025 CVPR] **MOS: Modeling Object-Scene Associations in Generalized Category Discovery.** [[paper]](https://arXiv.org/pdf/2503.12035) [[code]](https://github.com/JethroPeng/MOS)
7. [2024 NIPS] **Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels** [[paper]](https://arxiv.org/pdf/2409.19846)  [[code]](https://github.com/cvlab-kaist/PixelCLIP)
8. [2024 NIPS] **Renovating Names in Open-Vocabulary Segmentation Benchmarks** [[paper]](https://arxiv.org/pdf/2403.09593)
9. [2025 NIPS] **E-SD³: Fine-Grained Confidence-Aware Fusion of SD3 for Zero-Shot Semantic Matching** [[paper]](https://neurips.cc/virtual/2025/poster/119532) [[code]](https://github.com/panda7777777/feature-fusion)
10. [2025 NIPS] **MLMP: Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation** [[paper]](https://arxiv.org/pdf/2505.21844v1) [[code]](https://github.com/dosowiechi/MLMP)
11. [2025 arXiv] **NERVE: Neighbourhood & Entropy-guided Random-walk for training free open-Vocabulary segmentation** [[paper]](https://arxiv.org/pdf/2511.08248v1) [[code]](https://github.com/kunal-mahatha/nerve/)
12. [2025 arXiv] **Exploring the Underwater World Segmentation without Extra Training** [[paper]](https://arxiv.org/pdf/2511.07923v1) [[code]](https://github.com/LiBingyu01/Earth2Ocean)

<a name="Zero_shot_classification"></a>
## Zero shot Classification / Test Time Adaptation
1. [2024 NIPS] **SpLiCE: Interpreting CLIP with Sparse Linear Concept Embeddings.** [[paper]](https://proceedings.NIPS.cc/paper_files/paper/2024/file/996bef37d8a638f37bdfcac2789e835d-Paper-Conference.pdf) [[code]](https://github.com/AI4LIFE-GROUP/SpLiCE)
2. [2024 NIPS] **Transclip: Boosting Vision-Language Models with Transduction.** [[paper]](https://arXiv.org/pdf/2406.01837) [[code]](https://github.com/MaxZanella/transduction-for-vlms)
3. [2025 CVPR] **StatA: Realistic Test-Time Adaptation of Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2501.03729) [[code]](https://github.com/MaxZanella/StatA)
4. [2023 AAAI] **CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention.** [[paper]](https://arXiv.org/pdf/2209.14169) [[code]](https://github.com/ZiyuGuo99/CALIP)
5. [2025 AAAI] **TIMO: Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP.** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/32534) [[code]](https://github.com/lyymuwu/TIMO)
6. [2025 CVPR] **COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation.** [[paper]](https://arXiv.org/pdf/2503.23388) [[code]](https://github.com/hf618/COSMIC)
7. [2024 CVPR] **Transductive Zero-Shot and Few-Shot CLIP.** [[paper]](https://arXiv.org/pdf/2405.18437) [[code]](https://github.com/SegoleneMartin/transductive-CLIP)
8. [2023 CVPR] **Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.** [[paper]](https://arXiv.org/pdf/2111.03930) [[code]](https://github.com/gaopengcuhk/Tip-Adapter)
9. [2024 ICLR] **GDA-CLIP: A hard-to-beat baseline for training-free clip-based adaptation.** [[paper]](https://arXiv.org/pdf/2402.04087) [[code]](https://github.com/mrflogs/ICLR24)
10. [2023 ICLR] **DCLIP: Visual Classification via Description from Large Language Models.** [[paper]](https://arXiv.org/pdf/2210.07183) [[code]](https://github.com/sachit-menon/classify_by_description_release)
11. [2023 ICCV] **CuPL: What does a platypus look like? Generating customized prompts for zero-shot image classification.** [[paper]](https://arXiv.org/pdf/2209.03320) [[code]](https://github.com/sarahpratt/CuPL)
12. [2024 CVPR] **On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?** [[paper]](https://arXiv.org/pdf/2405.02266) [[code]](https://github.com/MaxZanella/MTA)
13. [2024 NIPS] **Frustratingly Easy Test-Time Adaptation of Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2405.18330) [[code]](https://github.com/FarinaMatteo/zero)
14. [2024 NIPS] **BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping.** [[paper]](https://arXiv.org/pdf/2410.15430) [[code]](https://github.com/taolinzhang/BoostAdapter)
15. [2024 CVPR] **DMN: Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2403.17589) [[code]](https://github.com/YBZh/DMN)
16. [2023 ICCV] **Zero-Shot Composed Image Retrieval with Textual Inversion.** [[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Baldrati_Zero-Shot_Composed_Image_Retrieval_with_Textual_Inversion_ICCV_2023_paper.pdf) [[code]](https://github.com/miccunifi/SEARLE.)
17. [2025 CVPR] **Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval.** [[paper]](https://arXiv.org/pdf/2412.11077) [[code]](https://github.com/Pter61/osrcir)
18. [2024 NIPS] **Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting.** [[paper]](https://arXiv.org/pdf/2410.19294) [[code]](https://github.com/zhuhsingyuu/Frolic)
19. [2025 ICML] **From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection.** [[paper]](https://arXiv.org/pdf/2505.13233) [[code]](https://github.com/BIT-DA/ABS)
20. [2025 CVPR] **PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts.** [[paper]](https://arXiv.org/pdf/2308.01313) [[code]](https://github.com/umd-huang-lab/perceptionCLIP)
21. [2024 CVPR] **ZLaP: Label Propagation for Zero-shot Classification with Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2404.04072) [[code]](https://github.com/vladan-stojnic/ZLaP)
22. [2025 CVPRW] **TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification.** [[paper]](https://arXiv.org/pdf/2503.12206) [[code]](https://github.com/ans92/TLAC)
23. [2023 NIPS] **Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP.** [[paper]](https://arXiv.org/pdf/2310.19752) [[code]](https://github.com/idstcv/InMaP)
24. [2024 ICML] **Let Go of Your Labels with Unsupervised Transfer.** [[paper]](https://arXiv.org/pdf/2406.07236?) [[code]](https://github.com/mlbio-epfl/turtle)
25. [2025 CVPR] **ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models.** [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Bendou_ProKeR_A_Kernel_Perspective_on_Few-Shot_Adaptation_of_Large_Vision-Language_CVPR_2025_paper.pdf) [[code]](https://github.com/ybendou/ProKeR)
26. [2024 CVPR] **TDA: Efficient Test-Time Adaptation of Vision-Language Model.** [[paper]](https://arXiv.org/pdf/2403.18293) [[code]](https://github.com/kdiAAA/TDA)
27. [2024 arXiv] **DOTA: Distributional test-time adaptation of Vision-Language Models** [[paper]](https://arxiv.org/pdf/2409.19375)
28. [2023 ICCV] **Black Box Few-Shot Adaptation for Vision-Language models** [[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Ouali_Black_Box_Few-Shot_Adaptation_for_Vision-Language_Models_ICCV_2023_paper.pdf) [[code]](https://github.com/saic-fi/LFA)
29. [2025 ICCV] **Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks** [[paper]](https://arxiv.org/pdf/2509.16163v1)) [[code]](https://github.com/HettyPatel/TensorDefenseVLM)
30. [2025 arXiv] **Training-Free Pyramid Token Pruning for Efficient Large Vision-Language Models via Region, Token, and Instruction-Guided Importance** [[paper]](https://arxiv.org/pdf/2509.15704v1)
31. [2025 arXiv] **Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models** [[paper]](https://arxiv.org/pdf/2509.20751v1)
32. [2009 ICML] **Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs** [[paper]](https://icml.cc/Conferences/2009/papers/360.pdf)
33. [2010 JMLR] **Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data** [[paper]](https://jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf) 
34. [2023 CVPR] **noHub: Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings** [[paper]](https://arxiv.org/pdf/2303.09352) [[code]](https://github.com/uitml/noHub)
35. [2025 CVPR] **A Hubness Perspective on Representation Learning for Graph-Based Multi-View Clustering** [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_A_Hubness_Perspective_on_Representation_Learning_for_Graph-Based_Multi-View_Clustering_CVPR_2025_paper.pdf) [[code]](https://github.com/zmxu196/hubREP)
36. [2025 CVPR] **NeighborRetr: Balancing Hub Centrality in Cross-Modal Retrieval** [[paper]](https://arxiv.org/pdf/2503.10526) [[code]](https://github.com/zzezze/NeighborRetr/tree/main)
37. [2025 arXiv] **SeMoBridge: Semantic Modality Bridge for Efficient Few-Shot Adaptation of CLIP** [[paper]](https://arxiv.org/pdf/2509.26036v1) [[code]](https://github.com/christti98/semobridge)
38. [2023 ICCV] **Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement** [[paper]](https://arxiv.org/pdf/2304.01195) [[code]](https://github.com/yangyangyang127/APE)
39. [2025 arXiv] **SQUARE: Semantic Query-Augmented Fusion and Efficient Batch Reranking for Training-free Zero-Shot Composed Image Retrieval** [[paper]](https://arxiv.org/pdf/2509.26330v1)
40. [2025 arXiv] **Skip-It? Theoretical Conditions for Layer Skipping in Vision-Language Models** [[paper]](https://arxiv.org/pdf/2509.25584v1)
41. [2025 arXiv] **VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors** [[paper]](https://arxiv.org/pdf/2510.00458v1) [[code]](https://github.com/imatif17/VLOD-TTA)
42. [2025 arXiv] **Bayesian Test-time Adaptation for Object Recognition and Detection with Vision-language Models** [[paper]](https://arxiv.org/pdf/2510.02750v1)
43. [2025 arXiv] **Constructive distortion: improving MLLMS with attention-guided image warping** [[paper]](https://arxiv.org/pdf/2510.09741v1) [[code]](https://dwipddalal.github.io/Attwarp/)
44. [2025 NIPS] **MLMP: Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation** [[paper]](https://arxiv.org/pdf/2505.21844v1) [[code]](https://github.com/dosowiechi/MLMP)
45. [2025 arXiv] **Reorienting the Frozen Space: Training-Free Test-Time Adaptation by Geometric Transformation** [[paper]](https://openreview.net/attachment?id=nErnNhJx2o&name=pdf)
46. [2025 arXiv] **Online in-context distillation for low-resource vision language models** [[paper]](https://arxiv.org/pdf/2510.18117v1)
47. [2025 NIPS] **TOMCAT:Test-timeComprehensive Knowledge Accumulation for Compositional Zero-Shot Learning** [[paper]](https://arxiv.org/pdf/2510.20162v1) [[code]](https://github.com/xud-yan/TOMCAT)
48. [2025 arXiv] **Seeing but not believing: probing the disconnect betwwen visual attention and answer correctness in VLMs** [[paper]](https://arxiv.org/pdf/2510.17771v1)
49. [2025 arXiv] **Adapting Vision-Language Models Without Labels: A Comprehensive Survey** [[paper]](https://arxiv.org/pdf/2508.05547?) [[code]](https://github.com/tim-learn/Awesome-LabelFree-VLMs)
50. [2025 arXiv] **Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition** [[paper]](https://arxiv.org/pdf/2510.26466v1) [[code]](https://github.com/peipeng98)
51. [2025 arXiv] **A-TPT: Angular Diversity Calibration Properties for Test-Time Prompt Tuning of Vision-Language Models** [[paper]](https://arxiv.org/pdf/2510.26441v1)
52. [2025 ICLR] **Basis sharing: cross-layer parameter sharing for large language model compression** [[paper]](https://arxiv.org/pdf/2410.03765) [[code]](https://github.com/TUDa-HWAI/Basis_Sharing)
53. [2025 arXiv] **SegDebias: Test-Time Bias Mitigation for ViT-Based CLIP via Segmentation** [[paper]](https://arxiv.org/pdf/2511.00523v1)
54. [2025 NIPS] **Training-Free Test-Time Adaptation via Shape and Style Guidance for Vision-Language Models** [[paper]](https://openreview.net/attachment?id=OF7OLxvY0t&name=pdf)
55. [2025 NIPS] **ADAPT: Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment** [[paper]](https://arxiv.org/pdf/2508.15568) [[code]](https://github.com/AIM-SKKU/ADAPT)

<a name="Optimal_Transport"></a>
## Optimal Transport
1. [2022 AISTATS] **Sinkformers: Transformers with Doubly Stochastic Attention.** [[paper]](https://arXiv.org/pdf/2110.11773) [[code]](https://github.com/michaelsdr/sinkformers)
2. [2024 ECCV] **OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2403.14183) [[code]](https://github.com/cubeyoung/OTSeg)
3. [2025 CVPR] **POT: Prototypical Optimal Transport for Weakly Supervised Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_POT_Prototypical_Optimal_Transport_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.pdf) [[code]](https://github.com/jianwang91/POT)
4. [2025 CVPR] **RAM: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Transport.** [[paper]](https://arXiv.org/pdf/2503.15337) [[code]](https://github.com/EricTan7/RAM)
5. [2022 NIPS] **SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments.** [[paper]](https://arXiv.org/pdf/2006.09882) [[code]](https://github.com/facebookresearch/swav)
6. [2023 ICLR] **PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2210.01253) [[code]](https://github.com/CHENGY12/PLOT)
7. [2024 NIPS] **OTTER: Effortless Label Distribution Adaptation of Zero-shot Models.** [[paper]](https://arXiv.org/pdf/2404.08461) [[code]](https://github.com/sprocketlab/otter)
8. [2025 ICCV] **LaZSL: Intrepretable Zero-Shot Learning with Locally-Aligned Vision-Language Model.** [[paper]](https://arXiv.org/pdf/2506.23822) [[code]](https://github.com/shiming-chen/LaZSL)
9. [2025 CVPR] **Conformal Prediction for Zero-Shot Models.** [[paper]](https://arXiv.org/pdf/2505.24693) [[code]](https://github.com/jusiro/CLIP-Conformal)
10. [2025 ICML] **ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via α-β-Divergence.** [[paper]](https://arXiv.org/abs/2505.04560) [[code]](https://github.com/ghwang-s/abkd)
11. [2024 ICLR] **EMO: Earth mover distance optimization for auto-regessive language modeling.** [[paper]](https://arXiv.org/pdf/2310.04691) [[code]](https://github.com/DRSY/EMO)
12. [2025 CVPR] **RAM: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport.** [[paper]](https://arXiv.org/abs/2503.15337) [[code]](https://github.com/EricTan7/RAM)
13. [2025 ICCV] **Class Token as Proxy: Optimal Transport-assisted Proxy Learning for Weakly Supervised Semantic Segmentation.** [[paper]](https://iccv.thecvf.com/virtual/2025/poster/1933)
14. [2025 AAAI] **Training-free Open-Vocabulary Semantic Segmentation via Diverse Prototype Construction and Sub-region Matching** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/33137)
15. [2025 TPAMI] **Recent Advances in Optimal Transport for Machine Learning** [[paper]](https://arxiv.org/pdf/2306.16156)
16. [2024 CVPR] **SALAD: Optimal transport aggregation for visual place recognition** [[paper]](https://arxiv.org/pdf/2311.15937) [[code]](https://github.com/serizba/salad)
17. [2025 arXiv] **SPROUT: Training-free Nuclear Instance segmentation with automatic prompting** [[paper]](https://openreview.net/attachment?id=pqLlFR5ken&name=pdf)
18. [2020 NIPS] **Model Fusion via Optimal Transport** [[paper]](https://arxiv.org/pdf/1910.05653) [[code]](https://github.com/sidak/otfusion)
19. [2024 ICLR] **Transformer Fusion with Optimal Transport** [[paper]](https://arxiv.org/pdf/2310.05719) [[code]](https://github.com/graldij/transformer-fusion)
20. [2023 ICAC] **Att-Sinkhorn: Multimodal Alignment with Sinkhorn-based Deep Attention Architecture** [[paper]](https://ieeexplore.ieee.org/document/10275301)
21. [2025 NIPS] **Enhancing CLIP Robustness via Cross-Modality Alignment** [[paper]](https://arxiv.org/pdf/2510.24038v1)
22. [2024 ICLR] **Towards meta-pruning via optimal transport** [[paper]](https://arxiv.org/pdf/2402.07839) [[code]](https://github.com/alexandertheus/Intra-Fusion)
23. [2021 IJCNN] **Label Propagation Through Optimal Transport** [[paper]](https://arxiv.org/pdf/2110.01446) [[code]](https://github.com/MouradElHamri/OTP/tree/main)

<a name="WSSS"></a>
## Weakly Supervised Semantic Segmentation
1. [2022 CVPR] **Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers.** [[paper]](https://arXiv.org/pdf/2203.02664) [[code]](https://github.com/rulixiang/afa)
2. [2022 CVPR] **MCTFormer:Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2203.02891) [[code]](https://github.com/xulianuwa/MCTformer)
3. [2023 CVPR] **Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization.** [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.pdf) [[code]](https://github.com/xulianuwa/MMCST)
4. [2023 ICCV] **Spatial-Aware Token for Weakly Supervised Object Localization.** [[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Wu_Spatial-Aware_Token_for_Weakly_Supervised_Object_Localization_ICCV_2023_paper.pdf) [[code]](https://github.com/wpy1999/SAT)
5. [2023 CVPR] **Boundary-enhanced Co-training for Weakly Supervised Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf) [[code]](https://github.com/ShenghaiRong/BECO?tab=readme-ov-file)
6. [2023 CVPR] **ToCo:Token Contrast for Weakly-Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2303.01267) [[code]](https://github.com/rulixiang/ToCo)
7. [2023 arXiv] **MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2308.03005) [[code]](https://github.com/xulianuwa/MCTformer)
8. [2024 CVPR] **Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2406.11189v1) [[code]](https://github.com/zbf1991/WeCLIP)
9. [2024 CVPR] **DuPL: Dual Student with Trustworthy Progressive Learning for RobustWeakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2403.11184) [[code]](https://github.com/Wu0409/DuPL)
10. [2024 CVPR] **Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2403.07630) [[code]](https://github.com/Barrett-python/CPAL)
11. [2024 ECCV] **DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2409.15801)
12. [2024 CVPR] **Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2402.18467) [[code]](https://github.com/zwyang6/SeCo)
13. [2024 ECCV] **CoSa:Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2402.17891) [[code]](https://github.com/youshyee/CoSA)
14. [2024 IEEE] **SSC:Spatial Structure Constraints for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2401.11122) [[code]](https://github.com/NUST-Machine-Intelligence-Laboratory/SSC)
15. [2024 AAAI] **Progressive Feature Self-Reinforcement for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2312.08916) [[code]](https://github.com/Jessie459/feature-self-reinforcement)
16. [2024 CVPR] **Class Tokens Infusion for Weakly Supervised Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Yoon_Class_Tokens_Infusion_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.pdf) [[code]](https://github.com/yoon307/CTI)
17. [2024 CVPR] **SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2401.11719) [[code]](https://github.com/Barrett-python/SFC)
18. [2024 CVPR] **PSDPM:Prototype-based Secondary Discriminative Pixels Mining for Weakly Supervised Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_PSDPM_Prototype-based_Secondary_Discriminative_Pixels_Mining_for_Weakly_Supervised_Semantic_CVPR_2024_paper.pdf) [[code]](https://github.com/xinqiaozhao/PSDPM)
19. [2024 arXiv] **A Realistic Protocol for Evaluation of Weakly Supervised Object Localization.** [[paper]](https://arXiv.org/pdf/2404.10034) [[code]](https://github.com/shakeebmurtaza/wsol_model_selection)
20. [2025 AAAI] **MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2412.11076) [[code]](https://github.com/zwyang6/MoRe)
21. [2025 CVPR] **PROMPT-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis.** [[paper]](https://arXiv.org/pdf/2501.09333) [[code]](https://github.com/Imageomics/Prompt_CAM)
22. [2025 CVPR] **Exploring CLIP’s Dense Knowledge for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2503.20826) [[code]](https://github.com/zwyang6/ExCEL)
23. [2025 CVPR] **GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery.** [[paper]](https://arXiv.org/abs/2403.09974) [[code]](https://github.com/enguangW/GET)
24. [2025 arXiv] **TeD-Loc: Text Distillation for Weakly Supervised Object Localization.** [[paper]](https://arXiv.org/pdf/2501.12632) [[code]](https://github.com/shakeebmurtaza/TeDLOC)
25. [2025 arXiv] **Image Augmentation Agent for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2412.20439)
26. [2025 CVPR] **Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_Multi-Label_Prototype_Visual_Spatial_Search_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.pdf)
27. [2025 CVPRW] **Prompt Categories Cluster for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2412.13823)
28. [2025 arXiv] **No time to train! Training-Free Reference-Based Instance Segmentation.** [[paper]](https://arXiv.org/pdf/2507.02798) [[code]](https://github.com/miquel-espinosa/no-time-to-train)
29. [2025 TPAMI] **Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation** [[paper]](https://arxiv.org/pdf/2403.13225) [[code]](https://github.com/Luffy03/AGMM-SASS)

<a name="Graph_Structure"></a>
## Graph Structure
1. [2016 AAAI] **The Constrained Laplacian Rank Algorithm for Graph-Based Clustering.** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/10302) [[code]](https://github.com/convexfi/spectralGraphTopology/blob/97eee40c8aa30cd62fa01b6e9e01e20197fc7940/R/constrLaplacianRank.R)
2. [2016 IJCAI] **Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification.** [[paper]](https://www.ijcai.org/Proceedings/16/Papers/269.pdf)
3. [2023 NIPS] **GSLB: The Graph Structure Learning Benchmark.** [[paper]](https://arXiv.org/pdf/2310.05174) [[code]](https://github.com/GSL-Benchmark/GSLB)
4. [2024 AAAI] **Catalyst for Clustering-based Unsupervised Object Re-Identification: Feature Calibration.** [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/28092) [[code]](https://github.com/lhf12278/FCM-ReID)
5. [2025 ICLR] **Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model.** [[paper]](https://arXiv.org/pdf/2412.18303) [[code]](https://github.com/Yushu-Li/ECALP)
6. [2025 NIPS] **One Prompt Fits All: Universal Graph Adaptation for Pretrained Models** [[paper]](https://arxiv.org/pdf/2509.22416) [[code]](https://github.com/hedongxiao-tju/UniPrompt)
7. [2021 AAAI] **UMGF: Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance** [[paper]](https://cdn.aaai.org/ojs/17687/17687-13-21181-1-2-20210518.pdf) [[code]](https://github.com/TransformersWsz/UMGF)
8. [network] **GNN Club** [[paper]](https://www.gnn.club/?p=2170)

<a name="VPR"></a>
## Visual Place Recognition
1. [2022 CVPR] **CosPlace: Rethinking Visual Geo-localization for Large-Scale Applications.** [[paper]](https://arXiv.org/pdf/2204.02287) [[code]](https://github.com/gmberton/CosPlace)
2. [2024 CVPR] **CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition.** [[paper]](https://arXiv.org/pdf/2402.19231) [[code]](https://github.com/Lu-Feng/CricaVPR)
3. [2024 CVPR] **BoQ: A Place is Worth a Bag of Learnable Queries.** [[paper]](https://arXiv.org/pdf/2405.07364) [[code]](https://github.com/amaralibey/Bag-of-Queries)
4. [2024 NIPS] **SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition.** [[paper]](https://openreview.net/pdf?id=bZpZMdY1sj) [[code]](https://github.com/Lu-Feng/SuperVLAD)
5. [2024 ECCV] **Revisit Anything: Visual Place Recognition via Image Segment Retrieval.** [[paper]](https://arXiv.org/pdf/2409.18049) [[code]](https://github.com/AnyLoc/Revisit-Anything)
6. [2025 arXiv] **HypeVPR: Exploring Hyperbolic Space for Perspective to Equirectangular Visual Place Recognition.** [[paper]](https://arXiv.org/pdf/2506.04764) [[code]](https://github.com/suhan-woo/HypeVPR)
7. [2023 IROS] **Training-Free Attentive-Patch Selection for Visual Place Recognition.** [[paper]](https://ieeexplore.ieee.org/abstract/document/10342347)
8. [2024 CVPR] **SALAD: Optimal transport aggregation for visual place recognition** [[paper]](https://arxiv.org/pdf/2311.15937) [[code]](https://github.com/serizba/salad)
9. [2021 CVPRW] **CCT: Escaping the Big Data Paradigm with Compact Transformers** [[paper]](https://arxiv.org/pdf/2104.05704) [[code]](https://github.com/SHI-Labs/Compact-Transformers)

<a name="Token_fusion"></a>
## Token Mering, Clustering and Pruning
1. [2021 NIPS] **TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?** [[paper]](https://arXiv.org/pdf/2106.11297) [[code]](https://github.com/google-research/scenic/tree/main/scenic/projects/token_learner)
2. [2022 CVPR] **GroupViT: Semantic Segmentation Emerges from Text Supervision.** [[paper]](https://arXiv.org/pdf/2202.11094) [[code]](https://github.com/NVlabs/GroupViT)
3. [2022 CVPR] **MCTformer+: Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2308.03005) [[code]](https://github.com/xulianuwa/MCTformer)
4. [2023 CVPR] **BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.** [[paper]](https://arXiv.org/pdf/2301.12597) [[code]](https://github.com/salesforce/LAVIS/tree/main/projects/blip2)
5. [2023 ICCV] **Perceptual Grouping in Contrastive Vision-Language Models.** [[paper]](https://arXiv.org/abs/2210.09996)
6. [2023 ICLR] **GPVIT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation.** [[paper]](https://arXiv.org/pdf/2212.06795) [[code]](https://github.com/ChenhongyiYang/GPViT)
7. [2023 CVPR] **SAN: Side Adapter Network for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2302.12242) [[code]](https://github.com/MendelXu/SAN)
8. [2024 CVPR] **Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers.** [[paper]](https://arXiv.org/pdf/2403.10030) [[code]](https://github.com/mlvlab/MCTF)
9. [2024 CVPR] **Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding.** [[paper]](https://arXiv.org/pdf/2311.08046) [[code]](https://github.com/PKU-YuanGroup/Chat-UniVi)
10. [2024 ICLR] **LaVIT: Unified language-vision pretraining in LLM with dynamic discrete visual tokenization.** [[paper]](https://arXiv.org/pdf/2309.04669) [[code]](https://github.com/jy0205/LaVIT)
11. [2024 arXiv] **TokenPacker: Efficient Visual Projector for Multimodal LLM.** [[paper]](https://arXiv.org/abs/2407.02392) [[code]](https://github.com/CircleRadon/TokenPacker)
12. [2024 arXiv] **DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models.** [[paper]](https://arXiv.org/pdf/2405.20985) [[code]](https://github.com/yaolinli/DeCo)
13. [2024 CVPR] **Grounding Everything: Emerging Localization Properties in Vision-Language Transformers.** [[paper]](https://arXiv.org/pdf/2312.00878) [[code]](https://github.com/WalBouss/GEM)
14. [2025 CVPR] **PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models.** [[paper]](https://arXiv.org/pdf/2504.08966) [[code]](https://github.com/orailix/PACT/tree/main)
15. [2025 arXiv] **ZSPAPrune: zero-shot prompt-aware token pruning for vision-language models** [[paper]](https://arxiv.org/pdf/2510.17197v1)
16. [2025 NIPS] **Don't Just Chase “Highlighted Tokens” in MLLMs: Revisiting Visual Holistic Context Retention** [[paper]](https://arxiv.org/pdf/2510.02912) [[code]](https://github.com/obananas/HoloV)
17. [2021 NIPS] **DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification** [[paper]](https://arxiv.org/pdf/2106.02034) [[code]](https://github.com/raoyongming/DynamicViT)
18. [2022 CVPR] **A-ViT: Adaptive Tokens for Efficient Vision Transformer** [[paper]](https://arxiv.org/pdf/2112.07658) [[code]](https://a-vit.github.io/)
19. [2023 ICLR] **ToMe: Token merging: Your vit but faster token** [[paper]](https://arxiv.org/pdf/2210.09461) [[code]](https://github.com/facebookresearch/ToMe)

<a name="Segmentation_and_Detection"></a>
## Segmentation and Detection
1. [2015 CVPR] **FCN: Fully Convolutional Networks for Semantic Segmentation.** [[paper]](https://arXiv.org/abs/1411.4038) [[code]](https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn)
2. [2016 MICCAI] **UNet: Convolutional Networks for Biomedical Image Segmentation.** [[paper]](https://arXiv.org/pdf/1505.04597)
3. [2017 arXiv] **DeepLabV3: Rethinking atrous convolution for semantic image segmentation.** [[paper]](https://arXiv.org/pdf/1706.05587)
4. [2018 CVPR] **DeepLabV3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.** [[paper]](https://arXiv.org/pdf/1802.02611)
5. [2019 CVPR] **Semantic FPN: Panoptic Feature Pyramid Networks.** [[paper]](https://arXiv.org/pdf/1901.02446)
6. [2021 CVPR] **SETR: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.** [[paper]](https://arXiv.org/pdf/2012.15840) [[code]](https://github.com/fudan-zvg/SETR)
7. [2021 ICCV] **Segmenter: Transformer for Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2105.05633) [[code]](https://github.com/rstrudel/segmenter)
8. [2021 NIPS] **SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.** [[paper]](https://arXiv.org/pdf/2105.15203) [[code]](https://github.com/NVlabs/SegFormer)
9. [2021 CVPR] **MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2107.06278) [[code]](https://github.com/facebookresearch/MaskFormer)
10. [2022 CVPR] **Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation.** [[paper]](https://arXiv.org/pdf/2112.01527) [[code]](https://github.com/facebookresearch/Mask2Former)
11. [2024 CVPR] **Rein: Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2312.04265) [[code]](https://github.com/w1oves/Rein)
12. [2015 NIPS] **Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.** [[paper]](https://arXiv.org/pdf/1506.01497)
13. [2020 ECCV] **DETR: End-to-End Object Detection with Transformers.** [[paper]](https://arXiv.org/pdf/2005.12872) [[code]](https://github.com/facebookresearch/detr)
14. [2021 ICLR] **Deformable DETR: Deformable Transformers for End-to-End Object Detection.** [[paper]](https://arXiv.org/pdf/2010.04159) [[code]](https://github.com/fundamentalvision/Deformable-DETR)
15. [2023 ICLR] **DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.** [[paper]](https://arXiv.org/pdf/2203.03605) [[code]](https://github.com/IDEA-Research/DINO)
16. [2025 arXiv] **SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V3** [[paper]](https://arxiv.org/pdf/2509.00833) [[code]](https://github.com/script-Yang/segdino/tree/main)
17. [2024 ICLR] **FeatUp: A Model-Agnostic Framework for Features at Any Resolution** [[paper]](https://arxiv.org/pdf/2403.10516) [[code]](https://github.com/mhamilton723/FeatUp)
18. [2025 ICCV] **LoftUp: A Coordinate-Based Feature Upsampler for Vision Foundation Models** [[paper]](https://arxiv.org/abs/2504.14032) [[code]](https://github.com/andrehuang/loftup)
19. [2025 arXiv] **AnyUp: Universal Feature Upsampling** [[paper]](https://arxiv.org/pdf/2510.12764) [[code]](https://github.com/wimmerth/anyup)
20. [2025 arXiv] **Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling** [[paper]](https://arxiv.org/pdf/2511.16301) [[code]](https://github.com/seominseok0429/Upsample-Anything-A-Simple-and-Hard-to-Beat-Baseline-for-Feature-Upsampling)
21. **SigLIP: Sigmoid Loss for Language Image Pre-Training** [[paper]](https://arxiv.org/pdf/2303.15343) [[code]](https://github.com/google-research/big_vision/tree/main)
22. [2025 arXiv]  **SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features** [[paper]](https://arxiv.org/pdf/2502.14786) [[code]](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/README_siglip2.md)

<a name="Backbone"></a>
## Backbone
1. [2017 NIPS] **Transfomer: Attention Is All You Need.** [[paper]](https://arXiv.org/pdf/1706.03762) [[code]](https://github.com/tensorflow/tensor2tensor)
2. [2021 ICLR] **ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.** [[paper]](https://arXiv.org/pdf/2010.11929) [[code]](https://github.com/google-research/vision_transformer)
3. [2021 ICML] **DeiT: Training data-efficient image transformers & distillation through attention.** [[paper]](https://arXiv.org/pdf/2012.12877)
4. [2021 ICCV] **Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.** [[paper]](https://arXiv.org/pdf/2103.14030) [[code]](https://github.com/microsoft/Swin-Transformer)
5. [2021 NIPS] **Twins: Revisiting the Design of Spatial Attention in Vision Transformers.** [[paper]](https://arXiv.org/pdf/2104.13840) [[code]](https://github.com/Meituan-AutoML/Twins)
6. [2022 CVPR] **Hyperbolic Vision Transformers: Combining Improvements in Metric Learning.** [[paper]](https://arXiv.org/pdf/2203.10833) [[code]](https://github.com/htdt/hyp_metric)
7. [2022 ICLR] **BEiT: BERT Pre-Training of Image Transformers.** [[paper]](https://arXiv.org/pdf/2106.08254) [[code]](https://github.com/microsoft/unilm/tree/master/beit)
8. [2022 CVPR] **MAE: Masked Autoencoders Are Scalable Vision Learners.** [[paper]](https://arXiv.org/pdf/2111.06377) [[code]](https://github.com/facebookresearch/mae)
9. [2022 CVPR] **PoolFormer: MetaFormer is Actually What You Need for Vision.** [[paper]](https://arXiv.org/pdf/2111.11418) [[code]](https://github.com/sail-sg/poolformer)
10. [2022 NIPS] **SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2209.08575) [[code]](https://github.com/Visual-Attention-Network/SegNeXt)
11. [2023 ICCV] **OpenSeeD: A simple framework for open-vocabulary segmentation and detection.** [[paper]](https://arXiv.org/pdf/2303.08131) [[code]](https://github.com/IDEA-Research/OpenSeeD)
12. [2023 arXiv] **SAM: Segment Anything.** [[paper]](https://arXiv.org/pdf/2304.02643) [[code]](https://github.com/facebookresearch/segment-anything) [[demo]](https://segment-anything.com/demo)
13. [2024 arXiv] **SAM2: Segment Anything in Images and Videos.** [[paper]](https://arXiv.org/pdf/2408.00714) [[code]](https://github.com/facebookresearch/sam2) [[demo]](https://sam2.metademolab.com/)
14. [2026 ICRL] **SAM3: Segment Anything with Concepts** [[paper]](https://arxiv.org/pdf/2511.16719) [[code]](https://github.com/facebookresearch/sam3) [[demo]](https://segment-anything.com/) 
15. [2024 github] **SAM with text prompt** [[code]](https://github.com/luca-medeiros/lang-segment-anything)
16. [2025 NIPS] **OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts** [[paper]](https://arxiv.org/pdf/2507.05427)
17. [2025 ICCV] **E-SAM: Training-Free Segment Every Entity Model** [[paper]](https://arxiv.org/pdf/2503.12094)
18. [2021 ICCV] **DINOv1: Emerging Properties in Self-Supervised Vision Transformers** [[paper]](https://arxiv.org/pdf/2104.14294) [[code]](https://github.com/facebookresearch/dino)
19. [2023 TMLR] **DINOv2: Learning Robust Visual Features without Supervision** [[paper]](https://arxiv.org/pdf/2304.07193) [[code]](https://github.com/facebookresearch/dinov2)
20. [2025 arXiv] **DINOv3** [[paper]](https://arxiv.org/pdf/2508.10104) [[code]](https://github.com/facebookresearch/dinov3)
21. [2025 arXiv] **ViT3: Unlocking Test-Time Training in Vision** [[paper]](https://arxiv.org/pdf/2512.01643v1) [[code]](https://github.com/LeapLabTHU/ViTTT)
22. [2025 arXiv] **SAM3-I: Segment Anything with Instructions** [[paper]](https://arxiv.org/pdf/2512.04585) [[code]](https://github.com/debby-0527/SAM3-I)
23. [2025 arXiv] **In Pursuit of Pixel Supervision for Visual Pre-training** [[paper]](https://arxiv.org/pdf/2512.15715) [[code]](https://github.com/facebookresearch/pixio)

<a name="VLMs and MLLM"></a>
## VLMs and MLLM
1. [2021 ICML] **CLIP: Learning transferable visual models from natural language supervision.** [[paper]](https://arXiv.org/pdf/2103.00020) [[code]](https://github.com/OpenAI/CLIP)
2. [2022 IJCV] **CoOp: Learning to Prompt for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2109.01134) [[code]](https://github.com/KaiyangZhou/CoOp)
3. [2022 ECCV] **VPT: Visual Prompt Tuning.** [[paper]](https://arXiv.org/pdf/2203.12119) [[code]](https://github.com/kmnp/vpt)
4. [2022 ICLR] **LoRA: Low-Rank Adaptation of Large Language Models.** [[paper]](https://arXiv.org/pdf/2106.09685) [[code]](https://github.com/microsoft/LoRA)
5. [2022 NIPS] **TPT: Test-Time Prompt Tuning for Zero-shot Generalization in Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2209.07511) [[code]](https://github.com/azshue/TPT)
6. [2022 arXiv] **UPL: Unsupervised Prompt Learning for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2204.03649) [[code]](https://github.com/tonyhuang2022/UPL)
7. [2022 arXiv] **CLIPPR: Improving Zero-Shot Models with Label Distribution Priors.** [[paper]](https://arXiv.org/pdf/2212.00784) [[code]](https://github.com/jonkahana/CLIPPR)
8. [2022 CVPR] **CoCoOp: Conditional Prompt Learning for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2203.05557) [[code]](https://github.com/KaiyangZhou/CoOp)
9. [2023 CVPR] **TaskRes: Task Residual for Tuning Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2211.10277) [[code]](https://github.com/geekyutao/TaskRe)
10. [2023 ICML] **POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models.** [[paper]](https://arXiv.org/pdf/2305.00350) [[code]](https://github.com/korawat-tanwisuth/POUF)
11. [2023 NIPS] **Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning.** [[paper]](https://arXiv.org/pdf/2306.01669) [[code]](https://github.com/BatsResearch/menghini-NIPS23-code)
12. [2023 NIPS] **LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections.** [[paper]](https://arXiv.org/pdf/2305.18287) [[code]](https://github.com/jmiemirza/LaFTer)
13. [2023 PRCV] **Unsupervised Prototype Adapter for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2308.11507)
14. [2023 IJCV] **CLIP-Adapter: Better Vision-Language Models with Feature Adapters.** [[paper]](https://arXiv.org/pdf/2110.04544) [[code]](https://github.com/gaopengcuhk/CLIP-Adapter)
15. [2024 CVPR] **CODER: Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification.** [[paper]](https://arXiv.org/pdf/2404.17753) [[code]](https://github.com/YCaigogogo/CODER)
16. [2024 CVPR] **LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP.** [[paper]](https://arXiv.org/pdf/2404.02285) [[code]](https://github.com/FereshteShakeri/FewShot-CLIP-Strong-Baseline)
17. [2024 CVPR] **PromptKD: Unsupervised Prompt Distillation for Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2403.02781) [[code]](https://github.com/zhengli97/PromptKD)
18. [2024 CVPR] **Transfer CLIP for Generalizable Image Denoising.** [[paper]](https://arXiv.org/pdf/2403.15132) [[code]](https://github.com/alwaysuu/CLIPDenoising)
19. [2024 ECCV] **BRAVE: Broadening the visual encoding of vision-language model.** [[paper]](https://arXiv.org/pdf/2404.07204)
20. [2024 ICML] **Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data.** [[paper]](https://arXiv.org/pdf/2406.10502) [[code]](https://github.com/vanillaer/CPL-ICML2024)
21. [2024 CVPR] **CLIP-KD: An Empirical Study of CLIP Model Distillation.** [[paper]](https://arXiv.org/pdf/2307.12732) [[code]](https://github.com/winycg/CLIP-KD)
22. [2025 WACV] **DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2408.08855) [[code]](https://github.com/Externalhappy/DPA)
23. [2025 WACV] **Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models.** [[paper]](https://arXiv.org/pdf/2403.12952) [[code]](https://github.com/elaine-sui/TPS)
24. [2025 WACV] **LATTECLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts.** [[paper]](https://arXiv.org/pdf/2410.08211) [[code]](https://github.com/astra-vision/LatteCLIP)
25. [2025 ICLR] **Cross the GAP: Exposing the intra-modal misalignment in CLIP via modality inversion.** [[paper]](https://arXiv.org/pdf/2502.04263) [[code]](https://github.com/miccunifi/Cross-the-Gap)
26. [2025 ICLR] **CLIP’s Visual Embedding Projector is a Few-shot Cornucopia.** [[paper]](https://arXiv.org/pdf/2410.05270) [[code]](https://github.com/astra-vision/ProLIP)
27. [2025 CVPR] **DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers.** [[paper]](https://arXiv.org/pdf/2505.23694) [[code]](https://github.com/Noahsark/DA-VPT)
28. [2025 ICML] **Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models.** [[paper]](https://arXiv.org/pdf/2506.02557) [[code]](https://github.com/peterant330/KUEA)
29. [2025 CVPR] **Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification.** [[paper]](https://arXiv.org/pdf/2503.16873) [[code]](https://github.com/k0u-id/CCD)
30. [2024 CVPR] **Multi-Modal Adapter for Vision-Language Models.** [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_MMA_Multi-Modal_Adapter_for_Vision-Language_Models_CVPR_2024_paper.pdf) [[code]](https://github.com/ZjjConan/VLM-MultiModalAdapter)
31. [2025 arXiv] **DeFacto: Counterfactual Thinking with Images for Enforcing Evidence-Grounded and Faithful Reasoning** [[paper]](https://arxiv.org/pdf/2509.20912v1) [[code]](https://github.com/tinnel123666888/defacto)
32. [2025 arXiv] **Hierarchical representation matching for clip-based class-incremental learning** [[paper]](https://arxiv.org/pdf/2509.22645v1)
33. [2025 EMNLP]**From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-LENS** [[paper]](https://arxiv.org/pdf/2510.02292v1) [[code]](https://github.com/compling-wat/vlm-lens)
34. [2025 NIPS] **Approximate Domain Unlearning for Vision-Language Models** [[paper]](https://arxiv.org/pdf/2510.08132v1) [[code]](https://kodaikawamura.github.io/Domain_Unlearning/)
35. [2025 ICLR] **Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset** [[paper]](https://arxiv.org/pdf/2411.03554) [[code]](https://github.com/SaFoLab-WISC/FIUBench)
36. [2025 NIPS] **∆Energy: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization** [[paper]](https://arxiv.org/pdf/2510.11296v1) [[code]](https://github.com/LinLLLL/DeltaEnergy)
37. [2025 CVPR] **Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages** [[paper]](https://arxiv.org/pdf/2503.11609) [[code]](https://github.com/FarinaMatteo/rethinking_fewshot_vlms)
38. [2025 arXiv] **Exploring cross-modal flows for few-shot learning** [[paper]](https://arxiv.org/pdf/2510.14543)
39. [2025 arXiv] **VisCoP: Visual Probing for Domain Adapatation of Vision Language Models** [[paper]](https://arxiv.org/pdf/2510.13808v1) [[code]](https://github.com/dominickrei/VisCoP/)
40. [2025 arXiv] **ProCLIP: Progressive Vision-Language Alignment via LLM-based Embedder** [[paper]](https://arxiv.org/pdf/2510.18795v1) (https://github.com/VisionXLab/ProCLIP)
41. [2025 arXiv] **CARES:Context-AwareResolutionSelectorforVLMs** [[paper]](https://arxiv.org/pdf/2510.19496v1)
42. [2025 ICCV] **Controllable-LPMoE: Adapting to Challenging Object Segmentation via Dynamic Local Priors from Mixture-of-Experts** [[paper]](https://arxiv.org/pdf/2510.21114v1) [[code]](https://github.com/CSYSI/Controllable-LPMoE)
43. [2025 ICLR] **Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP** [[paper]](https://arxiv.org/pdf/2406.17639) [[code]](https://github.com/sarahESL/AlignCLIP)
44. [2025 arXiv] **Counteracting Matthew Effect in Self-Improvement of LVLMs through Head-Tail Re-balancing** [[paper]](https://arxiv.org/pdf/2510.26474v1)
45. [2025 arXiv] **Modality alignment across trees on herero deneous huperbolic manifolds** [[paper]](https://arxiv.org/pdf/2510.27391v1) [[code]](https://github.com/MCISLAB-Manifold-Learning/HypModalAlign)
46. [2026 ICLR] **SEPS: Semantic-enhanced patch slimming framework for fine-grained cross-modal alignment** [[paper]](https://arxiv.org/pdf/2511.01390v1) [[code]](https://github.com/Sweet4tars/seps)
47. [2025 arXiv] **OMEGA:Optimized Multimodal Position Encoding Index Derivation with Global Adaptive Scaling for Vision-Language Models** [[paper]](https://arxiv.org/pdf/2511.00821v1)
48. [2025 arXiv] **Test-Time Spectrum-Aware Latent Steering for Zero-Shot Generalization in Vision-Language Models** [[paper]](https://arxiv.org/pdf/2511.09809v1) [[code]](https://github.com/kdafnis/STS)
49. [2025 arXiv] **BRIDGE: Bridging Hidden States in Vision–Language Models** [[paper]](https://arxiv.org/pdf/2511.11526v1) [[code]](https://github.com/jfeinashley/BRIDGE)
50. [2026 AAAI] **BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-based Class-Incremental Learning** [[paper]](https://arxiv.org/pdf/2511.11421v1)
51. [2025 CVPR] **Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves** [[paper]](https://arxiv.org/pdf/2412.11509) [[code]](https://github.com/Koorye/SkipTuning)

<a name="open_vocabulary"></a>
## Open vocabulary
### segmentation
1. [2022 ICLR] **Lseg: Language-driven semantic segmentation (Supervised).** [[paper]](https://arXiv.org/pdf/2201.03546) [[code]](https://github.com/isl-org/lang-seg)
2. [2022 CVPR] **ZegFormer: Decoupling Zero-Shot Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2112.07910) [[code]](https://github.com/dingjiansw101/ZegFormer)
3. [2022 ECCV] **MaskCLIP+: Extract Free Dense Labels from CLIP.** [[paper]](https://arXiv.org/pdf/2112.01071) [[code]](https://github.com/chongzhou96/MaskCLIP)
4. [2022 ECCV] **ViL-Seg: Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embeddings.** [[paper]](https://arXiv.org/pdf/2207.08455v2)
5. [2022 CVPR] **GroupViT: Semantic Segmentation Emerges from Text Supervision (Open-Vocabulary Zero-Shot).** [[paper]](https://arXiv.org/pdf/2202.11094) [[code]](https://github.com/NVlabs/GroupViT)
6. [2022 ECCV] **OpenSeg: Scaling Open-Vocabulary Image Segmentation with Image-Level Labels.** [[paper]](https://arXiv.org/pdf/2112.12143)
7. [2023 CVPR] **FreeSeg: Unified, Universal, and Open-Vocabulary Image Segmentation.** [[paper]](https://arXiv.org/pdf/2303.17225) [[code]](https://github.com/bytedance/FreeSeg)
8. [2023 ICML] **SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation (Zero-Shot).** [[paper]](https://arXiv.org/pdf/2211.14813) [[code]](https://github.com/ArrowLuo/SegCLIP)
9. [2023 CVPR] **ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2212.03588) [[code]](https://github.com/ZiqinZhou66/ZegCLIP)
10. [2023 CVPR] **X-Decoder: Generalized Decoding for Pixel, Image, and Language.** [[paper]](https://arXiv.org/pdf/2212.11270) [[code]](https://github.com/microsoft/X-Decoder/tree/main)
11. [2023 CVPR] **ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models.** [[paper]](https://arXiv.org/pdf/2303.04803) [[code]](https://github.com/NVlabs/ODISE)
12. [2023 ICML] **MaskCLIP: Open-Vocabulary Universal Image Segmentation with MaskCLIP.** [[paper]](https://arXiv.org/pdf/2208.08984) [[code]](https://github.com/mlpc-ucsd/MaskCLIP)
13. [2023 CVPR] **SAN: Side Adapter Network for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2302.12242) [[code]](https://github.com/MendelXu/SAN)
14. [2024 ECCV] **CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation.** [[paper]](https://arXiv.org/pdf/2312.12359) [[code]](https://github.com/wysoczanska/clip_dinoiser)
15. [2024 CVPR] **SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation.** [[paper]](https://arXiv.org/pdf/2311.15537) [[code]](https://github.com/xb534/SED)
16. [2024 TPAMI] **Review: Towards Open Vocabulary Learning: A Survey.** [[paper]](https://arXiv.org/pdf/2306.15880) [[code]](https://github.com/jianzongwu/Awesome-Open-Vocabulary)
17. [2025 ICCV] **Unbiased Region-Language Alignment for Open-Vocabulary Dense Prediction.** [[paper]](https://arXiv.org/abs/2412.06244) [[code]](https://github.com/HVision-NKU/DenseVLM)
18. [2024 CVPR] **Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation.** [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Exploring_Regional_Clues_in_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2024_paper.pdf) [[code]](https://github.com/Jittor/JSeg)
19. [2024 ICLR] **CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction** [[paper]](https://arxiv.org/pdf/2310.01403) [[code]](https://github.com/wusize/CLIPSelf)
20. [2025 CVPR] **DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception.** [[paper]](https://arXiv.org/pdf/2505.04410) [[code]](https://github.com/xiaomoguhz/DeCLIP)
21. [2025 arXiv] **RefAM: Attention magnets for zero-shot referral segmentaion** [[paper]](https://arxiv.org/pdf/2509.22650v1) [[code]](https://github.com/Annusha/refam)
22. [2023 NIPS] **OpenMask3D: Open-Vocabulary 3D Instance Segmentation** [[paper]](https://arxiv.org/pdf/2306.13631) [[code]](https://github.com/OpenMask3D/openmask3d)
23. [2023 NIPS] **Weakly Supervised 3D Open-vocabulary Segmentation** [[paper]](https://arxiv.org/pdf/2305.14093) [[code]](https://github.com/Kunhao-Liu/3D-OVS)
24. [2025 NIPS] **LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation** [[paper]](https://arxiv.org/pdf/2510.25263v1) [[code]](https://insait-institute.github.io/langhops.github.io/)
25. [2025 TPAMI] **Low-Resolution Self-Attention for Semantic Segmentation** [[paper]](https://arxiv.org/pdf/2310.05026) [[code]](https://github.com/yuhuan-wu/LRFormer)

### object detection
1. [2021 CVPR] **Open-Vocabulary Object Detection Using Captions.** [[paper]](https://arXiv.org/pdf/2011.10678) [[code]](https://github.com/alirezazareian/ovr-cnn)
2. [2022 ICLR] **ViLD: Open-Vocabulary Object Detection via Vision and Language Knowledge Distillation.** [[paper]](https://arXiv.org/pdf/2104.13921) [[code]](https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/vild)
3. [2022 CVPR] **GLIP: Grounded Language-Image Pre-training.** [[paper]](https://arXiv.org/pdf/2112.03857) [[code]](https://github.com/microsoft/GLIP)
4. [2022 NIPS] **GLIPv2: Unifying Localization and Vision-Language Understanding.** [[paper]](https://arXiv.org/pdf/2206.05836) [[code]](https://github.com/microsoft/GLIP)
5. [2024 ICCV] **Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection** [[paper]](https://arxiv.org/pdf/2303.05499) [[code]](https://github.com/IDEA-Research/GroundingDINO)
6. [2025 arXiv] **cross-view open-vocabulary object detection in aerial imagery** [[paper]](https://openreview.net/attachment?id=QgTA3AvUC5&name=pdf)

<a name="Model_Merging"></a>
### Model Merging
1. [2024 CVPR] **Training Free Pretrained Model Merging** [[paper]](https://arxiv.org/pdf/2403.01753) [[code]](https://github.com/zju-vipa/training_free_model_merging)
2. [2024 ECCV] **Traiing-Free Model Merging for Multi-target Domain Adaptation** [[paper]](https://arxiv.org/pdf/2407.13771) [[code]](https://github.com/AIR-DISCOVER/Model-Merging-MTDA)
3. [2025 arXiv] **Training-free heterogeneous model merging** [[paper]](https://arxiv.org/pdf/2501.00061) [[code]](https://github.com/zju-vipa/training_free_heterogeneous_model_merging)
4. [2025 arXiv] **Training-free LLM Merging for Multi-task Learning** [[paper]](https://arxiv.org/pdf/2506.12379) [[code]](https://github.com/Applied-Machine-Learning-Lab/Hi-Merging)
5. [2025 arXiv] **SeMe: Training-Free Language Model Merging via Semantic Alignment** [[paper]](https://arxiv.org/pdf/2505.20144)
6. [2024 arXiv] **Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.** [[paper]](https://arxiv.org/pdf/2408.07666) [[code]](https://github.com/EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications)
7. [2025 arXiv] **TR-Merging: Training-free Router for Model Merging** [[paper]](https://openreview.net/attachment?id=nErnNhJx2o&name=pdf)
8. [2024 ICML] **Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch** [[paper]](https://arxiv.org/pdf/2311.03099) [[code]](https://github.com/yule-BUAA/MergeLM)
9. [2020 NIPS] **Model Fusion via Optimal Transport** [[paper]](https://arxiv.org/pdf/1910.05653) [[code]](https://github.com/sidak/otfusion)
10. [2024 ICLR] **Transformer Fusion with Optimal Transport** [[paper]](https://arxiv.org/pdf/2310.05719) [[code]](https://github.com/graldij/transformer-fusion)
11. [2022 ICML] **Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time** [[paper]](https://arxiv.org/pdf/2203.05482) [[code]](https://github.com/mlfoundations/model-soups)
12. [2023 ICLR] **Editing Models with Task Arithmetic** [[paper]](https://arxiv.org/pdf/2212.04089) [[code]](https://github.com/mlfoundations/task_vectors)
13. [2023 NIPS] **TIES-Merging: Resolving Interference When Merging Models** [[paper]](https://arxiv.org/pdf/2306.01708) [[code]](https://github.com/prateeky2806/ties-merging)
14. [2020 NIPS] **FedDF: Ensemble Distillation for Robust Model Fusion in Federated Learning** [[paper]](https://arxiv.org/pdf/2006.07242) [[code]](https://github.com/epfml/federated-learning-public-code/tree/master)
15. [2024 WACV] **FusionDistill: Consolidating separate degradations model via weights fusion and distillation** [[paper]](https://openaccess.thecvf.com/content/WACV2024W/VAQ/papers/Daultani_Consolidating_Separate_Degradations_Model_via_Weights_Fusion_and_Distillation_WACVW_2024_paper.pdf) [[code]](https://github.com/dineshdaultani/FusionDistill)
16. [2023 arXiv] **Deep Model Fusion: A Survey** (https://arxiv.org/pdf/2309.15698)
17. [2024 arXiv] **Multimodal Alignment and Fusion: A Survey** [[paper]](https://arxiv.org/pdf/2411.17040)

<a name="Active"></a>
### Active learning/ Data Selection
1. [2025 arXiv] **Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories** [[paper]](https://arxiv.org/pdf/2510.01454v1) [[code]](https://bigml-cs-ucla.github.io/XMAS-project-page/)
2. [2025 arXiv] **AdaRD-key: Adaptive Relevance-Diversity Keyframe Sampling for Long-form Video understanding** [[paper]](https://arxiv.org/pdf/2510.02778v1) [[code]](https://github.com/Xian867/AdaRD-Key)
3. [2025 arXiv] **Diffusion Synthesis: Data Factory with Minimal Human Effort Using VLMs** [[paper]](https://arxiv.org/pdf/2510.05722v1)

<a name="Time_series"></a>
### Time series
1. [2025 ICLR] **FreDF: Learning to Forecast in the Frequency Domain** [[paper]](https://arxiv.org/pdf/2402.02399) [[code]](https://github.com/Master-PLC/FreDF)
2. [2025 ICML] **Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting** [[paper]](https://arxiv.org/pdf/2502.04395) [[code]](https://github.com/CityMind-Lab/ICML25-TimeVLM)
3. [2024 AAAI] **MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting** [[paper]](https://arxiv.org/pdf/2401.00423) [[code]](https://github.com/YoZhibo/MSGNet)
4. [2025 arXiv] **Data efficient any transformer-to-mamba distillation via attention bridge** [[paper]](https://arxiv.org/pdf/2510.19266v1) [[code]](https://github.com/wph6/CAB)

<a name="Deep_Estimation"></a>
## Deep Estimation
1. [2014 NIPS] **Depth Map Prediction from a Single Image using a Multi-Scale Deep Network** [[paper]](https://arxiv.org/pdf/1406.2283) [[code]](https://github.com/hjimce/Depth-Map-Prediction)
2. [2015 ICCV] **Predicting Depth, Surface, Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture** [[paper]](https://arxiv.org/pdf/1411.4734)
3. [2017 TCSVT] **Estimating depth from monocular images as classification using deep fully convolutional residual networks** [[paper]](https://arxiv.org/pdf/1605.02305)
4. [2021 ICCV] **DPT: Vision Transformers for Dense Prediction** [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Ranftl_Vision_Transformers_for_Dense_Prediction_ICCV_2021_paper.pdf) [[code]](https://github.com/isl-org/DPT)
5. [2022 TPAMI] **MiDaS: Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer** [[paper]](https://arxiv.org/pdf/1907.01341v3) [[code]](https://github.com/isl-org/MiDaS) 
6. [2024 CVPR] **Depth Anything V1: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data** [[paper]](https://arxiv.org/pdf/2401.10891) [[code]](https://github.com/LiheYoung/Depth-Anything)
7. [2024 NIPS] **Depth Anything V2** [[paper]](https://arxiv.org/pdf/2406.09414) [[code]](https://github.com/DepthAnything/Depth-Anything-V2)
8. [2025 CVPR] **Video Depth Anything: Consistent Depth Estimation for Super-Long Videos** [[paper]](https://arxiv.org/pdf/2501.12375) [[code]](https://github.com/DepthAnything/Video-Depth-Anything)
9. [2025 ICCV] **Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation** [[paper]](https://arxiv.org/pdf/2510.09320v1) [[code]](https://github.com/Zhangwenyao1/Hybrid-depth)

<a name="Other"></a>
## Other Technologies
1. [2016 CVPRW] **pixel shuffle** [[paper]](https://arXiv.org/pdf/1609.05158) 
2. [2019 ICCV] **CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features** [[paper]](https://arxiv.org/pdf/1905.04899) [[code]](https://github.com/clovaai/CutMix-PyTorch)
3. [2020 NIPS] **DDPM: Denoising Diffusion Probabilistic Models.** [[paper]](https://arXiv.org/pdf/2006.11239) [[code]](https://github.com/hojonathanho/diffusion)
4. [2025 NIPS] **TTRL: Test-Time Reinforcement Learning** [[paper]](https://arxiv.org/pdf/2504.16084) [[code]](https://github.com/PRIME-RL/TTRL)
5. [2025 NIPS] **Unified Reinforcement and Imitation Learning for Vision-Language Models** [[paper]](https://arxiv.org/pdf/2510.19307v1) [[code]](https://byungkwanlee.github.io/RIL-page/)
6. [2021 ICCV] **Mean Shift for Self-Supervised Learning** [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Koohpayegani_Mean_Shift_for_Self-Supervised_Learning_ICCV_2021_paper.pdf) [[code]](https://github.com/UMBCvision/MSF)
7. [2024 CVPR] **Contrastive Mean-Shift Learning for Generalized Category Discovery** [[paper]](https://arxiv.org/pdf/2404.09451) [[code]](https://github.com/sua-choi/CMS)
8. [2025 arXiv] **Emu3.5: Native Multimodal Models are World Learners** [[paper]](https://arxiv.org/pdf/2510.26583v1) [[code]](https://github.com/baaivision/Emu3.5)

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