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https://github.com/zhangchbin/awesome-continual-segmentation
This repo is a collection of AWESOME things about continual semantic segmentation, including papers, code, demos, etc. Feel free to pull request and star.
https://github.com/zhangchbin/awesome-continual-segmentation
List: awesome-continual-segmentation
Last synced: 16 days ago
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This repo is a collection of AWESOME things about continual semantic segmentation, including papers, code, demos, etc. Feel free to pull request and star.
- Host: GitHub
- URL: https://github.com/zhangchbin/awesome-continual-segmentation
- Owner: zhangchbin
- License: mit
- Created: 2022-04-08T14:57:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-11T14:29:08.000Z (over 1 year ago)
- Last Synced: 2024-04-10T10:14:42.789Z (9 months ago)
- Size: 29.3 KB
- Stars: 14
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-continual-segmentation - This repo is a collection of AWESOME things about continual semantic segmentation, including papers, code, demos, etc. Feel free to pull request and star. (Other Lists / Monkey C Lists)
README
# awesome-continual-segmentation
This repo is a collection of AWESOME things about continual semantic segmentation, including papers, code, demos, etc. Feel free to pull request and star.
## 2024
- Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.14142)
- Strike a Balance in Continual Panoptic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.16354)
- Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.09047)
- Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.09838)
- Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.13363)
- Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing Images [[ACM MM 2024]](https://arxiv.org/pdf/2407.14242)
- Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception [[arXiv.2407]](https://arxiv.org/pdf/2407.18145)
- Balanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentation [[arXiv.2408]](https://arxiv.org/pdf/2408.01356)
- Continual Segmentation with Disentangled Objectness Learning and Class Recognition [[CVPR 2024]](https://openaccess.thecvf.com/content/CVPR2024/papers/Gong_Continual_Segmentation_with_Disentangled_Objectness_Learning_and_Class_Recognition_CVPR_2024_paper.pdf)
- Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation [[CVPR 2024]](https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Incremental_Nuclei_Segmentation_from_Histopathological_Images_via_Future-class_Awareness_and_CVPR_2024_paper.pdf)
## 2023
- Continual Semantic Segmentation with Automatic Memory Sample Selection [[CVPR 2023]](https://arxiv.org/pdf/2304.05015)
- Endpoints Weight Fusion for Class Incremental Semantic Segmentation [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf)
- Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.pdf)
- Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation [[WACV 2023]](https://arxiv.org/abs/2210.07207)
- Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory [[TPAMI 2023]](https://arxiv.org/abs/2309.15413)
- Federated Incremental Semantic Segmentation [[CVPR 2023]](https://arxiv.org/pdf/2304.04620)
- Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Kalb_Principles_of_Forgetting_in_Domain-Incremental_Semantic_Segmentation_in_Adverse_Weather_CVPR_2023_paper.html)
- Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.html)
- Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation [[CVPR 2023]](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.pdf)
- Preparing the Future for Continual Semantic Segmentation [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Lin_Preparing_the_Future_for_Continual_Semantic_Segmentation_ICCV_2023_paper.pdf)
- Label-Guided Knowledge Distillation for Continual Semantic Segmentation on 2D Images and 3D Point Clouds [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Yang_Label-Guided_Knowledge_Distillation_for_Continual_Semantic_Segmentation_on_2D_Images_ICCV_2023_paper.pdf)
- Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Ji_Continual_Segment_Towards_a_Single_Unified_and_Non-forgetting_Continual_Segmentation_ICCV_2023_paper.pdf)
- CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation [[ICCV 2023]](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_CoinSeg_Contrast_Inter-_and_Intra-_Class_Representations_for_Incremental_Segmentation_ICCV_2023_paper.pdf)
## 2022
- Representation Compensation Networks for Continual Semantic Segmentation [[CVPR 2022]](https://arxiv.org/abs/2203.05402) [[PyTorch]](https://github.com/zhangchbin/RCIL)
- Incremental Learning in Semantic Segmentation from Image Labels [[CVPR 2022]](https://arxiv.org/pdf/2112.01882.pdf)
- Continual Semantic Segmentation via Structure Preserving and Projected Feature Alignment [[ECCV 2022]](https://dl.acm.org/doi/10.1007/978-3-031-19818-2_20)
- RBC: Rectifying the Biased Context in Continual Semantic Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2203.08404v1.pdf)
- Self-training for Class-incremental Semantic Segmentation [[TNNLS 2022]](https://arxiv.org/abs/2012.03362) [PyTorch]
- Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation [[TPAMI 2022]](https://arxiv.org/pdf/2203.14098.pdf) [[PyTorch]]
- Continual Attentive Fusion for Incremental Learning in Semantic Segmentation[[TMM 2022]](https://arxiv.org/pdf/2202.00432.pdf)
- Decomposed knowledge distillation for class-incremental semantic segmentation [[NeurIPS 2022]](https://proceedings.neurips.cc/paper_files/paper/2022/file/439bf902de1807088d8b731ca20b0777-Paper-Conference.pdf)
- ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation [[NeurIPS]](https://arxiv.org/abs/2210.06816)
- Mining unseen classes via regional objectness: A simple baseline for incremental segmentation [[NeurIPS]](https://proceedings.neurips.cc/paper_files/paper/2022/file/99b419554537c66bf27e5eb7a74c7de4-Paper-Conference.pdf)
## 2021
- SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning [[NeurIPS 2021]](https://proceedings.neurips.cc/paper/2021/file/5a9542c773018268fc6271f7afeea969-Paper.pdf) [[PyTorch]](https://github.com/clovaai/SSUL)
- RECALL: Replay-based Continual Learning in Semantic Segmentation[[ICCV 2021]](https://arxiv.org/abs/2108.03673v1)
- PLOP: Learning without Forgetting for Continual Semantic Segmentation [[CVPR 2021]](https://arxiv.org/abs/2011.11390) [[PyTorch]](https://github.com/arthurdouillard/CVPR2021_PLOP)
- Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations [[CVPR2021]](https://arxiv.org/abs/2103.06342) [[PyTorch]](https://github.com/LTTM/SDR)
- An EM Framework for Online Incremental Learning of Semantic Segmentation [[ACM MM 2021]](https://arxiv.org/pdf/2108.03613.pdf) [[PyTorch]](https://github.com/Rhyssiyan/Online.Inc.Seg-Pytorch)
- SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning [[NeurIPS 2021]](https://proceedings.neurips.cc/paper/2021/file/5a9542c773018268fc6271f7afeea969-Paper.pdf) [[PyTorch]](https://github.com/clovaai/SSUL)
- Incremental Few-Shot Instance Segmentation [[CVPR 2021]](https://arxiv.org/abs/2108.03673v1)
- Unsupervised Model Adaptation for Continual Semantic Segmentation [[AAAI 2021]](https://arxiv.org/abs/2009.12518)
- A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation [[AAAI 2021]](https://www.aaai.org/AAAI21Papers/AAAI-2989.ZhengE.pdf)
## 2020
- Modeling the Background for Incremental Learning in Semantic Segmentation [[CVPR 2020]](https://arxiv.org/abs/2002.00718) [[PyTorch]](https://github.com/fcdl94/MiB)
## 2019
- Incremental Learning Techniques for Semantic Segmentation [[ICCV Workshop 2019]](https://arxiv.org/abs/1907.13372) [[PyTorch]](https://github.com/LTTM/IL-SemSegm)