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WSOD-Paper-List

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A paper list of state-of-the-art weakly supervised object detection or localization.

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# Weakly supervised object detection (WSOD)
A paper list of state-of-the-art weakly supervised object detection (**WSOD**) and weakly supervised object localization (**WSOL**).

## 2023
**WSOD**
- **[DETR-GA]** DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection. **[CVPR 2023]** [[`pdf`](https://arxiv.org/abs/2304.07082)]
- **[WS-DETR]** Scaling Novel Object Detection with Weakly Supervised Detection Transformers. **[WACV 2023]** [[`pdf`](https://arxiv.org/pdf/2207.05205.pdf)]  [[`official code-torch`](https://github.com/tmlabonte/weakly-supervised-DETR)]
- **[WSOD-CBL]** Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection. **[ICCV 2023]** [[`pdf`](https://arxiv.org/pdf/2308.05991.pdf)]  [[`official code-torch`](https://github.com/Yinyf0804/WSOD-CBL)]

**WSOL**
- **[CATR]** Category-aware Allocation Transformer for Weakly Supervised Object Localization. **[ICCV 2023]** [[`pdf`](https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_Category-aware_Allocation_Transformer_for_Weakly_Supervised_Object_Localization_ICCV_2023_paper.pdf)]  [[`official code-torch`](https://github.com/zhiweichen0012/CATR)]
- **[GenPromp]** Generative Prompt Model for Weakly Supervised Object Localization. **[ICCV 2023]** [[`pdf`](https://arxiv.org/pdf/2307.09756.pdf)]  [[`official code-torch`](https://github.com/callsys/GenPromp)]
- **[SAT]** Spatial-Aware Token for Weakly Supervised Object Localizatio. **[ICCV 2023]** [[`pdf`](https://arxiv.org/pdf/2303.10438.pdf)]  [[`official code-torch`](https://github.com/wpy1999/SAT)]

## 2022
**WSOD**
- **[SoS]** Salvage of Supervision in Weakly Supervised Detection. **[CVPR 2022]** [[`pdf`](https://arxiv.org/abs/2106.04073)]  [[`official code-torch`](https://github.com/suilin0432/SoS-WSOD)]
- **[H2FA_R-CNN]** H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-domain Weakly Supervised Object Detection. **[CVPR 2022]** [[`pdf`](https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.pdf)]  [[`official code-torch`](https://github.com/XuYunqiu/H2FA_R-CNN)]
- **[OD-WSCL]** Object Discovery via Contrastive Learning for Weakly Supervised Object Detection. **[ECCV 2022]** [[`pdf`](https://arxiv.org/abs/2208.07576)]  [[`official code-torch`](https://github.com/jinhseo/OD-WSCL)]
- **[Mi_max]** Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts. **[CVIU 2022]** [[`pdf`](https://arxiv.org/abs/2008.01178)]  [[`official code-tf`](https://github.com/ngonthier/Mi_max)]
- **[BUAA]** Selecting High-Quality Proposals for Weakly Supervised Object Detection With Bottom-Up Aggregated Attention and Phase-Aware Loss. **[TIP 2022]** [[`pdf`](https://ieeexplore.ieee.org/document/10003247)] [[`official code-torch`](https://github.com/Horatio9702/BUAA_PALoss)]
- **[FI-WSOD]** FI-WSOD: Foreground Information Guided Weakly Supervised Object Detection. **[TMM 2022]** [[`pdf`](https://ieeexplore.ieee.org/document/9854139)]
- **[CPNet]** Weakly Supervised Object Detection with Class Prototypical Network. **[TMM 2022]** [[`pdf`](https://ieeexplore.ieee.org/abstract/document/9826802)]
- **[H2FA R-CNN]** H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-domain Weakly Supervised Object Detection. **[CVPR 2022]** [[`pdf`](https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.pdf)] [[`official code-torch`](https://github.com/XuYunqiu/H2FA_R-CNN)]

**WSOL**
- **[DA-WSOL]** Weakly Supervised Object Localization as Domain Adaption. **[CVPR 2022]** [[`pdf`](https://arxiv.org/abs/2203.01714)]  [[`official code-torch`](https://github.com/zh460045050/DA-WSOL_CVPR2022)]
- **[ViTOL]** ViTOL: Vision Transformer for Weakly Supervised Object Localization. **[CVPR 2022]** [[`pdf`](https://arxiv.org/abs/2204.06772)]  [[`official code-torch`](https://github.com/Saurav-31/ViTOL)]
- Background Activation Suppression for Weakly Supervised Object Localization. **[CVPR 2022]** [[`pdf`](https://arxiv.org/abs/2112.00580)]  [[`official code-torch`](https://github.com/wpy1999/BAS)]
- **[CREAM]** CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping. **[CVPR 2022]** [[`pdf`](https://arxiv.org/pdf/2205.13922.pdf)]  [[`official code-torch`](https://github.com/Jazzcharles/CREAM)]
- Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization. **[CVPR 2022]** [[`pdf`](https://arxiv.org/abs/2204.00220)]
- **[C2AM]** C2AM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation. **[CVPR 2022]** [[`pdf`](https://arxiv.org/abs/2203.13505)]  [[`official code-torch`](https://github.com/CVI-SZU/CCAM)]

## 2021
**WSOD**
- Instance Mining with Class Feature Banks for Weakly Supervised Object Detection. **[AAAI 2021]** [[`pdf`](https://ojs.aaai.org/index.php/AAAI/article/view/16429)]]
- **[GradingNet]** GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates. **[AAAI 2021]** [[`pdf`](https://ojs.aaai.org/index.php/AAAI/article/view/16261)]
- **[LBBA-boosted WSOD]** Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters. **[ICCV 2021]** [[`pdf`](https://arxiv.org/abs/2108.01499)]  [[`official code-torch`](https://github.com/DongSky/lbba_boosted_wsod)]
- **[PDSL]** Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation. **[ICCV 2021]** [[`pdf`](https://openaccess.thecvf.com/content/ICCV2021/papers/Shen_Parallel_Detection-and-Segmentation_Learning_for_Weakly_Supervised_Instance_Segmentation_ICCV_2021_paper.pdf)]  [[`official code-torch`](https://github.com/shenyunhang/PDSL)]
- **[AIR]** Multiple Instance Detection Networks With Adaptive Instance Refinement. **[TMM 2021]** [[`pdf`](https://ieeexplore.ieee.org/document/9612070)]
- **[P-MIDN]** Pyramidal multiple instance detection network with mask guided self-correction for weakly supervised object detection. **[TIP 2021]** [[`pdf`](https://ieeexplore.ieee.org/document/9353395)]

**WSOL**
- **[SPOL]** Shallow Feature Matters for Weakly Supervised Object Localization. **[CVPR 2021]** [[`pdf`](https://arxiv.org/abs/2108.00873)]  [[`official code-torch`](https://github.com/weijun88/SPOL)]
- **[SPA]** Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization. **[CVPR 2021]** [[`pdf`](https://arxiv.org/abs/2103.04523)]  [[`official code-torch`](github.com/Panxjia/SPA)]
- **[SLT-Net]** Strengthen Learning Tolerance for Weakly Supervised Object Localization. **[CVPR 2021]** [[`pdf`](https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.pdf)]  [[`official code-torch`](https://github.com/gyguo/SLT-Net)]
- **[FAM]** Foreground Activation Maps for Weakly Supervised Object Localization. **[ICCV 2021]** [[`pdf`](https://openaccess.thecvf.com/content/ICCV2021/html/Meng_Foreground_Activation_Maps_for_Weakly_Supervised_Object_Localization_ICCV_2021_paper.html)]
- **[ORNet]** Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization. **[ICCV 2021]** [[`pdf`](https://arxiv.org/abs/2110.05741)]  [[`official code-torch`](https://github.com/Sierkinhane/ORNet)]

## 2020
**WSOD**
- High-Quality Proposals for Weakly Supervised Object Detection. **[TIP 2020]** [[`pdf`](https://ieeexplore.ieee.org/document/9069411)]
- Progressive Representation Adaptation for Weakly Supervised Object Localization. **[PAMI 2020]** [[`pdf`](https://arxiv.org/abs/1710.04647)]
- **[MIST]** Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection. **[CVPR 2020]** [[`pdf`](https://arxiv.org/abs/2004.04725)]  [[`official code`](https://github.com/NVlabs/wetectron)] 
- **[SLV]** SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection. **[CVPR 2020]** [[`pdf`](https://arxiv.org/abs/2006.12884)]
- **[OIM]** Object Instance Mining for Weakly Supervised Object Detection. **[AAAI 2020]** [[`pdf`](https://arxiv.org/abs/2002.01087)]  [[`official code-caffe`](https://github.com/bigvideoresearch/OIM)]
- **[BOICR]** Distilling Knowledge from Refinement in Multiple Instance Detection Networks. **[Deep Vision 2020 CVPR workshop]** [[`pdf`](https://arxiv.org/abs/2004.10943)] [[`official code-torch`](https://github.com/luiszeni/Boosted-OICR)]
- **[WSOD Transfer]** Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer. **[ECCV 2020]** [[`pdf`](http://arxiv.org/abs/2007.07986)] [[`official code-torch`](https://github.com/mikuhatsune/wsod_transfer)]
- Enabling Deep Residual Networks for Weakly Supervised Object Detection. **[ECCV 2020]** [[`pdf`](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530120.pdf)] [[`official code-caffe`](https://github.com/shenyunhang/DRN-WSOD)] [[`official code-torch`](https://github.com/shenyunhang/DRN-WSOD-pytorch/tree/DRN-WSOD/projects/WSL)]
- **[CASD]** Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection. **[NeurIPS 2020]** [[`pdf`](http://arxiv.org/abs/2010.12023)] [[`official code-torch`](https://github.com/DeLightCMU/CASD)]
- **[UWSOD]** UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection. **[NeurIPS 2020]** [[`pdf`](https://proceedings.neurips.cc/paper_files/paper/2020/file/4e0928de075538c593fbdabb0c5ef2c3-Paper.pdf)] [[`official code-torch`](https://github.com/shenyunhang/UWSOD)]
- Weakly Supervised Object Detection Using Proposal- and Semantic-Level Relationships. **[TPAMI 2020]** [[`pdf`](https://ieeexplore.ieee.org/document/9303471)]

**WSOL**
- **[EIL]** Erasing Integrated Learning : A Simple yet Effective Approach for Weakly Supervised Object Localization. **[ CVPR 2020]** [[`pdf`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Mai_Erasing_Integrated_Learning_A_Simple_Yet_Effective_Approach_for_Weakly_CVPR_2020_paper.pdf)]
- **[PSOL]** Rethinking the Route Towards Weakly Supervised Object Localization. **[CVPR 2020]** [[`pdf`](http://arxiv.org/abs/2002.11359)]
- **[GC-Net]** Geometry Constrained Weakly Supervised Object Localization. **[ECCV 2020]** [[`pdf`](https://arxiv.org/abs/2007.09727)]  [[`official code-torch`](https://github.com/lwzeng/GC-Net)]
- **[I2C]** Inter-Image Communication for Weakly Supervised Localization. **[ECCV 2020]** [[`pdf`](https://arxiv.org/abs/2008.05096)]  [[`official code-torch`](https://github.com/xiaomengyc/I2C)]
- **[wsolevaluation]** Evaluating Weakly Supervised Object Localization Methods Right. **[CVPR 2020]** [[`pdf`](https://arxiv.org/abs/2001.07437)]  [[`official code-torch`](https://github.com/clovaai/wsolevaluation)]

## 2019
**WSOD**
- **[ADWSOD]** Activity Driven Weakly Supervised Object Detection. **[CVPR 2019]** [[`pdf`](https://arxiv.org/abs/1803.11365)]
- Utilizing the Instability in Weakly Supervised Object Detection. **[CVPR 2019]** [[`pdf`](https://arxiv.org/abs/1906.06023)]
- **[WS-JDS]** Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation. **[CVPR 2019]** [[`pdf`](http://www.vie.group/media/pdf/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.pdf)] [[`official code-caffe`](https://github.com/shenyunhang/WS-JDS)]
- **[W-RPN]** You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection. **[CVPR 2019]** [[`pdf`](http://openaccess.thecvf.com/content_CVPR_2019/papers/Singh_You_Reap_What_You_Sow_Using_Videos_to_Generate_High_CVPR_2019_paper.pdf)] [[`official code`](https://github.com/kkanshul/w-rpn)]
- Dissimilarity Coefficient based Weakly Supervised Object Detection. **[CVPR 2019]** [[`pdf`](https://arxiv.org/abs/1811.10016)]
- **[C-MIL]** C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection. **[CVPR 2019]** [[`pdf`](https://arxiv.org/abs/1904.05647)]  [[`official code-torch`](https://github.com/Winfrand/C-MIL)]
- **[WSOD2]** WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection. **[ICCV 2019]** [[`pdf`](https://arxiv.org/abs/1909.04972)]
- Object-Aware Instance Labeling for Weakly Supervised Object Detection. **[ICCV 2019]** [[`pdf`](https://arxiv.org/abs/1908.03792)]
- **[C-MIDN]** C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection. **[ICCV 2019]** [[`pdf`](http://openaccess.thecvf.com/content_ICCV_2019/papers/Gao_C-MIDN_Coupled_Multiple_Instance_Detection_Network_With_Segmentation_Guidance_for_ICCV_2019_paper.pdf)]
- Towards Precise End-to-End Weakly Supervised Object Detection Network. **[ICCV 2019]** [[`pdf`](https://arxiv.org/abs/1911.12148)]
- **[SDCN]** Weakly Supervised Object Detection With Segmentation Collaboration. **[ICCV 2019]** [[`pdf`](https://arxiv.org/abs/1904.00551)]
- **[MELM]** Min-entropy latent model for weakly supervised object detection. **[TPAMI 2019]** [[`pdf`](https://arxiv.org/abs/1902.06057)]  [[`official code-torch`](https://github.com/WinFrand/MELM)]
- Self Paced Deep Learning for Weakly Supervised Object Detection. **[PAML 2019]** [[`pdf`](https://arxiv.org/abs/1605.07651)]  [[`official code-caffe`](https://github.com/moinnabi/SelfPacedDeepLearning)]
- **[CSC]** Category-Aware Spatial Constraint for Weakly Supervised Detection. **[ TIP 2019]** [[`pdf`](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8809899&tag=1)] [[`official code-caffe`](https://github.com/shenyunhang/CSC)]
- **[PSNet]** WSOD with PSNet and Box Regression. **[arXiv 2019]** [[`pdf`](https://arxiv.org/abs/1911.11512)]
- **[C-SPCL]** Leveraging Prior-Knowledge for Weakly Supervised Object Detection Under a Collaborative Self-Paced Curriculum Learning Framework. **[IJCV 2019]** [[`pdf`](https://link.springer.com/article/10.1007/s11263-018-1112-4)]

**WSOL**
- **[ADL]** Attention-based dropout layer for weakly supervised object localization. **[CVPR 2019]** [[`pdf`](https://arxiv.org/abs/1908.10028)]  [[`official code-tf`](https://github.com/junsukchoe/ADL)]
- **[DANet]** DANet: Divergent Activation for Weakly Supervised Object Localization. **[ICCV 2019]** [[`pdf`](https://openaccess.thecvf.com/content_ICCV_2019/papers/Xue_DANet_Divergent_Activation_for_Weakly_Supervised_Object_Localization_ICCV_2019_paper.pdf)]  [[`official code-torch`](https://github.com/xuehaolan/DANet)]

## 2018
**WSOD**
- **[PCL]** PCL: Proposal Cluster Learning for Weakly Supervised Object Detection. **[TPAMI 2018]** [[`pdf`](https://arxiv.org/abs/1807.03342)]  [[`official code-caffe`](https://github.com/ppengtang/oicr/tree/pcl)] [[`official code-torch`](https://github.com/ppengtang/pcl.pytorch)]
- Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning. **[CVPR 2018]** [[`pdf`](https://arxiv.org/abs/1802.09129)]
- Zigzag Learning for Weakly Supervised Object Detection. **[CVPR 2018]** [[`pdf`](https://arxiv.org/abs/1804.09466)]
- **[W2F]** W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection. **[CVPR 2018]** [[`pdf`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_W2F_A_Weakly-Supervised_CVPR_2018_paper.pdf)]
- Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation. **[CVPR 2018]** [[`pdf`](https://arxiv.org/abs/1803.11365)]  [[`official code`](https://github.com/naoto0804/cross-domain-detection)]
- Weakly-supervised object detection via mining pseudo ground truth bounding-boxes. **[PR 2018]** [[`pdf`](https://www.sciencedirect.com/science/article/pii/S0031320318302346)]
- Weakly Supervised Region Proposal Network and Object Detection. **[ECCV 2018]** [[`pdf`](https://openaccess.thecvf.com/content_ECCV_2018/papers/Peng_Tang_Weakly_Supervised_Region_ECCV_2018_paper.pdf)]
- **[TS2C]** TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection. **[ECCV 2018]** [[`pdf`](https://arxiv.org/pdf/1807.04897.pdf)]
- **[MELM]** Min-Entropy Latent Model for Weakly Supervised Object Detection. **[CVPR 2018]** [[`pdf`](https://openaccess.thecvf.com/content_cvpr_2018/papers/Wan_Min-Entropy_Latent_Model_CVPR_2018_paper.pdf)]  [[`official code-torch`](https://github.com/WinFrand/MELM)]
- **[WSCDN]** Collaborative Learning for Weakly Supervised Object Detection. **[IJCAI 2018]** [[`pdf`](https://arxiv.org/abs/1802.03531)]
- **[WSRPN]** Weakly Supervised Region Proposal Network and Object Detection. **[ECCV 2018]** [[`pdf`](https://www.ecva.net/papers/eccv_2018/papers_ECCV/papers/Peng_Tang_Weakly_Supervised_Region_ECCV_2018_paper.pdf)]

**WSOL**
- **[ACoL]** Adversarial Complementary Learning for Weakly Supervised Object Localization. **[CVPR 2018]** [[`pdf`](https://arxiv.org/abs/1804.06962)]
- **[SPG]** Self-produced Guidance for Weakly-supervised Object Localization. **[ECCV 2018]** [[`pdf`](https://arxiv.org/abs/1807.08902)]  [[`official code-torch`](https://github.com/xiaomengyc/SPG)]
- Generative Adversarial Learning Towards Fast Weakly Supervised Detection. **[CVPR 2018]** [[`pdf`](https://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_Generative_Adversarial_Learning_CVPR_2018_paper.pdf)]
- **[C-WSL]** C-WSL: Count-guided Weakly Supervised Localization. **[ECCV 2018]** [[`pdf`](https://arxiv.org/abs/1711.05282)]

## 2017
**WSOD**
- **[WCCN]** Weakly Supervised Cascaded Convolutional Networks. **[CVPR 2017]** [[`pdf`](https://arxiv.org/abs/1611.08258)]
- **[OICR]** Multiple Instance Detection Network with Online Instance Classifier Refinement. **[CVPR 2017]** [[`pdf`](https://arxiv.org/abs/1704.00138)] [[`official code-caffe`](https://github.com/ppengtang/oicr)] [[`unofficial code-torch`](https://github.com/CatOneTwo/OICR-PyTorch)] [[`unofficial code-torch`](https://github.com/jd730/OICR-pytorch)]
- Deep Self-Taught Learning for Weakly Supervised Object Localization. **[CVPR 2017]** [[`pdf`](https://arxiv.org/abs/1704.05188)]
- **[GPMIL]** Variational Bayesian Multiple Instance Learning With Gaussian Processes. **[CVPR 2017]** [[`pdf`](http://openaccess.thecvf.com/content_cvpr_2017/papers/Haussmann_Variational_Bayesian_Multiple_CVPR_2017_paper.pdf)] [[`official code`](https://github.com/manuelhaussmann/vgpmil)]
- A Dual-Network Progressive Approach to Weakly Supervised Object Detection. **[MM 2017]** [[`pdf`](https://dl.acm.org/doi/pdf/10.1145/3123266.3123455)]
- **[MICL]** Multiple Instance Curriculum Learning for Weakly Supervised Object Detection. **[BMVC 2017]** [[`pdf`](https://arxiv.org/abs/1711.09191)]
- **[SPN]** Soft Proposal Networks for Weakly Supervised Object Localization. **[ICCV 2017]** [[`pdf`](https://arxiv.org/abs/1709.01829)]  [[`official code-torch`](https://github.com/ZhouYanzhao/SPN/)]

**WSOL**
- Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning. **[TPAMI 2017]** [[`pdf`](https://arxiv.org/abs/1503.00949)]
- **[Grad-CAM]** Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. **[CVPR 2017]** [[`pdf`]()]  [[`official code`](https://github.com/ramprs/grad-cam/)]
- **[WILDCAT]** WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation. **[CVPR 2017]** [[`pdf`](https://openaccess.thecvf.com/content_cvpr_2017/papers/Durand_WILDCAT_Weakly_Supervised_CVPR_2017_paper.pdf)]
- **[TP-WSL]** Two-Phase Learning for Weakly Supervised Object Localization. **[ICCV 2017]** [[`pdf`](https://arxiv.org/abs/1708.02108)]

## 2016
**WSOD**
- **[WSDDN]** Weakly Supervised Deep Detection Networks. **[CVPR 2016]** [[`pdf`](https://arxiv.org/abs/1511.02853)] [[`official code-matlab`](https://github.com/hbilen/WSDDN)] [[`unofficial code-torch`](https://github.com/adursun/wsddn.pytorch)] [[`unofficial code-torch`](https://github.com/CatOneTwo/WSDDN-PyTorch)]
- **[ProNet]** ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks. **[CVPR 2016]** [[`pdf`](https://arxiv.org/abs/1511.03776)]
- Weakly Supervised Object Localization with Progressive Domain Adaptation. **[CVPR 2016]** [[`pdf`](http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Weakly_Supervised_Object_CVPR_2016_paper.pdf)] [[`official code-caffe`](https://github.com/jbhuang0604/WSL)]
- **[WELDON]** WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks. **[CVPR 2016]** [[`pdf`](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Durand_WELDON_Weakly_Supervised_CVPR_2016_paper.pdf)]  [[`official code-torch`](https://github.com/Cadene/weldon.torch)]
- **[ContextLocNet]** ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization. **[ECCV 2016]** [[`pdf`](https://arxiv.org/abs/1609.04331)]  [[`official code-caffe`](https://github.com/vadimkantorov/contextlocnet)]
- Weakly Supervised Localization using Deep Feature Maps. **[ECCV 2016]** [[`pdf`](https://arxiv.org/abs/1603.00489)]
- Weakly Supervised Object Localization Using Size Estimates. **[ECCV 2016]** [[`pdf`](https://arxiv.org/abs/1608.04314)]
- Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning. **[IJCAI 2016]** [[`pdf`](https://arxiv.org/abs/1703.01290)]

**WSOL**
- **[CAM]** Learning Deep Features for Discriminative Localization. **[CVPR 2016]** [[`pdf`](https://arxiv.org/abs/1512.04150)]  [[`unofficial code-tf`](https://github.com/SafwanMahmood/Learning-Deep-Features-for-Discriminative-Localization)]

## Before 2015
**WSOD**
- Weakly Supervised Localization and Learning with Generic Knowledge. **[IJCV 2012]**  [[`pdf`](https://www.tpbin.com/Uploads/Subjects/b978d659-6777-4cfd-8691-cf50e2061f13.pdf)]
- Multi-fold MIL Training for Weakly Supervised Object Localization. **[CVPR 2014]**  [[`pdf`](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Cinbis_Multi-fold_MIL_Training_2014_CVPR_paper.pdf)]
- On learning to localize objects with minimal supervision. **[ICML 2014]** [[`pdf`](https://arxiv.org/abs/1403.1024)]
- Is object localization for free? - Weakly-supervised learning with convolutional neural networks. **[CVPR 2015]** [[`pdf`](https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_075.pdf)]
- Weakly supervised object detection with convex clustering. **[CVPR 2015]** [[`pdf`](https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_118_ext.pdf)]

**WSOL**
- Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation. **[ICCV 2013]** [[`pdf`](https://arxiv.org/abs/1705.03372)]
- **[ICL]** Weakly Supervised Object Localization with Latent Category Learning. **[ECCV 2014]** [[`pdf`](https://arxiv.org/abs/1902.06057)]

## Relevant Repositories
- [Deep Learning Object Detection](https://github.com/hoya012/deep_learning_object_detection) ![Github stars](https://img.shields.io/github/stars/hoya012/deep_learning_object_detection.svg)
- [Weakly Supervised Semantic Segmentation](https://github.com/JackieZhangdx/WeakSupervisedSegmentationList) ![Github stars](https://img.shields.io/github/stars/JackieZhangdx/WeakSupervisedSegmentationList.svg)
- [Weakly-/Semi-Supervised Learning in Computer Vision](https://github.com/TinyGrass/CV_WSSL) ![Github stars](https://img.shields.io/github/stars/TinyGrass/CV_WSSL.svg)
- [Weakly-Supervised-Object-Localization](https://github.com/xiaomengyc/Weakly-Supervised-Object-Localization) ![Github stars](https://img.shields.io/github/stars/xiaomengyc/Weakly-Supervised-Object-Localization.svg)
- [WSOD Paper List](https://github.com/wcy1122/WSOD_Paper_List) ![Github stars](https://img.shields.io/github/stars/wcy1122/WSOD_Paper_List.svg)

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# Contact & Feedback
If you have any suggestions about this list, feel free to mail me :)
- Email: 21112034@bjtu.edu.cn
- Pull Request: https://github.com/CatOneTwo/Weakly-Supervised-Object-Detection/pulls

WSOD-Paper-List

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WSOD-Paper-List

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