Awesome-Incremental-Learning-For-Semantic-Segmentation

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Awesome-Incremental-Learning-For-Semantic-Segmentation

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A list of papers and codes about Incremental/Continual Semantic Segmentation

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# Awesome-Incremental-Learning-For-Semantic-Segmentation
A list of papers and codes about Incremental/Continual Semantic Segmentation.

### Incremental Semantic Segmentation

- Continual Segmentation with Disentangled Objectness Learning and Class Recognition[[paper](https://arxiv.org/abs/2403.03477)]. CVPR 2024
- Gradient-Semantic Compensation for Incremental Semantic Segmentation[[paper](https://arxiv.org/pdf/2307.10822.pdf)]. TMM 2023
- Inherit With Distillation and Evolve With Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory[[paper](https://ieeexplore.ieee.org/document/10120962)]. TPAMI 2023
- Coinseg: Contrast inter-and intra-class representations for incremental segmentation[[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_CoinSeg_Contrast_Inter-_and_Intra-_Class_Representations_for_Incremental_Segmentation_ICCV_2023_paper.pdf)] [[code](https://github.com/zkzhang98/coinseg)]. ICCV 2023
- Label-Guided Knowledge Distillation for Continual Semantic Segmentation on 2D Images and 3D Point Clouds[[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Yang_Label-Guided_Knowledge_Distillation_for_Continual_Semantic_Segmentation_on_2D_Images_ICCV_2023_paper.pdf)] [[code](https://github.com/Ze-Yang/LGKD)]. ICCV 2023
- Preparing the Future for Continual Semantic Segmentation[[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Lin_Preparing_the_Future_for_Continual_Semantic_Segmentation_ICCV_2023_paper.pdf)]. ICCV 2023
- Incrementer: Transformer for Class-Incremental Semantic Segmentation with Knowledge Distillation Focusing on Old Class[[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.pdf)]. CVPR 2023
- CoMFormer: Continual Learning in Semantic and Panoptic Segmentation[[paper](https://arxiv.org/abs/2211.13999)] [[code](https://github.com/fcdl94/CoMFormer)]. CVPR 2023
- Continual Semantic Segmentation with Automatic Memory Sample Selection[[paper](https://arxiv.org/abs/2304.05015)]. CVPR 2023
- Endpoints Weight Fusion for Class Incremental Semantic Segmentation[[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf)]. CVPR 2023
- SATS: Self-Attention Transfer for Continual Semantic Segmentation[[paper](https://arxiv.org/abs/2203.07667)] [[code](https://github.com/QIU023/SATS_Continual_Semantic_Seg)]. PR 2023 
- Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation[[paper](https://arxiv.org/abs/2203.14098)] [[code](https://github.com/ygjwd12345/UCD)]. TPAMI 2022
- Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation[[paper](https://arxiv.org/abs/2210.07207)] [[code](https://github.com/dfki-av/AWT-for-CISS)]. WACV 2023
- Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation[[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Phan_Class_Similarity_Weighted_Knowledge_Distillation_for_Continual_Semantic_Segmentation_CVPR_2022_paper.pdf)] [[code](https://github.com/HieuPhan33/REMINDER)]. CVPR 2022
- Continual Attentive Fusion for Incremental Learning in Semantic Segmentation[[paper](https://arxiv.org/abs/2202.00432)] [[code](https://github.com/ygjwd12345/CAF)]. TMM 2022
- Self-Training for Class-Incremental Semantic Segmentation[[paper](https://arxiv.org/abs/2012.03362)]. TNNLS 2022
- Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation[[paper](https://arxiv.org/abs/2210.05941)] [[code](https://github.com/cvlab-yonsei/DKD)]. NeurIPS 2022
- Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation[[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/99b419554537c66bf27e5eb7a74c7de4-Paper-Conference.pdf)] [[code](https://github.com/zkzhang98/MicroSeg)]. NeurIPS 2022
- RBC: Rectifying the Biased Context in Continual Semantic Segmentation[[paper](https://arxiv.org/abs/2203.08404)] [[code](https://github.com/sntc129/RBC)]. ECCV 2022
- Multi-Domain Incremental Learning for Semantic Segmentation[[paper](https://arxiv.org/abs/2110.12205)] [[code](https://github.com/prachigarg23/MDIL-SS)]. WACV 2022
- SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning[[paper](https://arxiv.org/abs/2106.11562)] [[code](https://github.com/clovaai/SSUL)]. NeurIPS 2021
- Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations[[paper](https://arxiv.org/abs/2103.06342)] [[code](https://github.com/LTTM/SDR)]. CVPR 2021

####  Replay-based

- An EM Framework for Online Incremental Learning of Semantic Segmentation[[paper](https://arxiv.org/abs/2108.03613)]. ACM MM 2021
- RECALL: Replay-based Continual Learning in Semantic Segmentation[[paper](https://arxiv.org/abs/2108.03673)] [[code](https://github.com/LTTM/RECALL)]. ICCV 2021
- iCaRL: Incremental Classifier and Representation Learning[[paper](https://arxiv.org/abs/1611.07725)] [[code](https://github.com/srebuffi/iCaRL)]. CVPR 2017

#### Distillation-based

- Representation Compensation Networks for Continual Semantic Segmentation[[paper](https://arxiv.org/abs/2203.05402)] [[code](https://github.com/zhangchbin/RCIL)]. CVPR 2022
- Knowledge Distillation for Incremental Learning in Semantic Segmentation[[paper](https://arxiv.org/abs/1911.03462)] [[code](https://lttm.dei.unipd.it/paper_data/KDSemantic)]. CVIU 2021
- PLOP: Learning without Forgetting for Continual Semantic Segmentation[[paper](https://arxiv.org/abs/2011.11390)] [[code](https://github.com/fcdl94/MiB)]. CVPR 2021
- Modeling the Background for Incremental Learning in Semantic Segmentation[[paper](https://arxiv.org/abs/2002.00718)] [[code](https://github.com/fcdl94/MiB)]. CVPR 2020
- Incremental Learning Techniques for Semantic Segmentation[[paper](https://arxiv.org/abs/1907.13372)] [[code](https://github.com/LTTM/IL-SemSegm)]. ICCV 2019

#### Dataset
- CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning[[paper](https://openaccess.thecvf.com/content/CVPR2023W/CLVision/papers/Nazemi_CLVOS23_A_Long_Video_Object_Segmentation_Dataset_for_Continual_Learning_CVPRW_2023_paper.pdf)] [[dataset](https://github.com/Amir4g/CLVOS23)]

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