├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Chang-Bin Zhang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # awesome-continual-segmentation 2 | This repo is a collection of AWESOME things about continual semantic segmentation, including papers, code, demos, etc. Feel free to pull request and star. 3 | 4 | ## 2024 5 | - Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.14142) 6 | - Strike a Balance in Continual Panoptic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.16354) 7 | - Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.09047) 8 | - Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.09838) 9 | - Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation [[ECCV 2024]](https://arxiv.org/pdf/2407.13363) 10 | - Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing Images [[ACM MM 2024]](https://arxiv.org/pdf/2407.14242) 11 | - Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception [[arXiv.2407]](https://arxiv.org/pdf/2407.18145) 12 | - Balanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentation [[arXiv.2408]](https://arxiv.org/pdf/2408.01356) 13 | - 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) 14 | - 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) 15 | 16 | 17 | 18 | 19 | ## 2023 20 | - Continual Semantic Segmentation with Automatic Memory Sample Selection [[CVPR 2023]](https://arxiv.org/pdf/2304.05015) 21 | - 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) 22 | - 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) 23 | - Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation [[WACV 2023]](https://arxiv.org/abs/2210.07207) 24 | - Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory [[TPAMI 2023]](https://arxiv.org/abs/2309.15413) 25 | - Federated Incremental Semantic Segmentation [[CVPR 2023]](https://arxiv.org/pdf/2304.04620) 26 | - 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) 27 | - 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) 28 | - 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) 29 | - 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) 30 | - 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) 31 | - 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) 32 | - 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) 33 | 34 | ## 2022 35 | - Representation Compensation Networks for Continual Semantic Segmentation [[CVPR 2022]](https://arxiv.org/abs/2203.05402) [[PyTorch]](https://github.com/zhangchbin/RCIL) 36 | - Incremental Learning in Semantic Segmentation from Image Labels [[CVPR 2022]](https://arxiv.org/pdf/2112.01882.pdf) 37 | - 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) 38 | - RBC: Rectifying the Biased Context in Continual Semantic Segmentation [[ECCV 2022]](https://arxiv.org/pdf/2203.08404v1.pdf) 39 | - Self-training for Class-incremental Semantic Segmentation [[TNNLS 2022]](https://arxiv.org/abs/2012.03362) [PyTorch] 40 | - Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation [[TPAMI 2022]](https://arxiv.org/pdf/2203.14098.pdf) [[PyTorch]] 41 | - Continual Attentive Fusion for Incremental Learning in Semantic Segmentation[[TMM 2022]](https://arxiv.org/pdf/2202.00432.pdf) 42 | - Decomposed knowledge distillation for class-incremental semantic segmentation [[NeurIPS 2022]](https://proceedings.neurips.cc/paper_files/paper/2022/file/439bf902de1807088d8b731ca20b0777-Paper-Conference.pdf) 43 | - ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation [[NeurIPS]](https://arxiv.org/abs/2210.06816) 44 | - 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) 45 | 46 | 47 | 48 | ## 2021 49 | - 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) 50 | - RECALL: Replay-based Continual Learning in Semantic Segmentation[[ICCV 2021]](https://arxiv.org/abs/2108.03673v1) 51 | - PLOP: Learning without Forgetting for Continual Semantic Segmentation [[CVPR 2021]](https://arxiv.org/abs/2011.11390) [[PyTorch]](https://github.com/arthurdouillard/CVPR2021_PLOP) 52 | - 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) 53 | - 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) 54 | - 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) 55 | - Incremental Few-Shot Instance Segmentation [[CVPR 2021]](https://arxiv.org/abs/2108.03673v1) 56 | - Unsupervised Model Adaptation for Continual Semantic Segmentation [[AAAI 2021]](https://arxiv.org/abs/2009.12518) 57 | - A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation [[AAAI 2021]](https://www.aaai.org/AAAI21Papers/AAAI-2989.ZhengE.pdf) 58 | 59 | 60 | ## 2020 61 | - Modeling the Background for Incremental Learning in Semantic Segmentation [[CVPR 2020]](https://arxiv.org/abs/2002.00718) [[PyTorch]](https://github.com/fcdl94/MiB) 62 | 63 | ## 2019 64 | - Incremental Learning Techniques for Semantic Segmentation [[ICCV Workshop 2019]](https://arxiv.org/abs/1907.13372) [[PyTorch]](https://github.com/LTTM/IL-SemSegm) 65 | --------------------------------------------------------------------------------