└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Continual Test-Time Adaptation 2 | **This repository organizes papers related to the CTTA(Continual-Test-Time-Adaptation) by SUSI-Lab.** 3 | 4 | 5 | ## 2024 6 | (**Arxiv 24**) Hybrid-TTA: Continual Test-time Adaptation via Dynamic Domain Shift Detection [[paper](https://arxiv.org/pdf/2409.08566)][[code](https://sites.google.com/view/hybrid-tta/)] \ 7 | (**Arxiv 24**) Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation [[paper](https://arxiv.org/pdf/2405.16486)][[code](https://github.com/RoyZry98/MoASE-Pytorch)] \ 8 | (**Arxiv 24**) Variational Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2402.08182)] \ 9 | (**Arxiv 24**) Controllable Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2405.14602)][[code](https://github.com/RenshengJi/C-CoTTA)] \ 10 | (**Arxiv 24**) Mitigating the Bias in the Model for Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2403.01344)] \ 11 | (**Arxiv 24**) Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2406.02609)] \ 12 | (**Arxiv 24**) Parameter-Selective Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2407.02253)][[code](https://github.com/JiaxuTian/PSMT)] \ 13 | (**Arxiv 24**) Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments [[paper](https://arxiv.org/pdf/2406.16439)] \ 14 | (**Arxiv 24**) Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech [[paper](https://arxiv.org/pdf/2406.11064)] \ 15 | (**Arxiv 24**) Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2406.10737)][[code](https://github.com/zybeich/DPCore)] \ 16 | (**Arxiv 24**) Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions [[paper](https://arxiv.org/pdf/2406.06607)] 17 | 18 | 19 | (**ECCV 24**) Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation[[paper](https://arxiv.org/pdf/2407.09367)][[code](https://github.com/z1358/OBAO)] 20 | 21 | 22 | (**ICML 24**) BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation [[paper](https://arxiv.org/pdf/2402.08712)][[code](https://github.com/daeunni/becotta)] 23 | 24 | 25 | (**CVPR 24**) Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2312.12480.pdf)] \ 26 | (**CVPR 24**) Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation [[paper](https://arxiv.org/pdf/2311.18363.pdf)][[code](https://github.com/Chen-Ziyang/VPTTA)] \ 27 | (**CVPR 24**) A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability \ 28 | (**CVPR 24**) What, How, and When Should Object Detectors Update in Continually Changing Test Domains? 29 | 30 | 31 | 32 | 33 | (**WACV 24**) Continual Test-time Domain Adaptation via Dynamic Sample Selection [[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Wang_Continual_Test-Time_Domain_Adaptation_via_Dynamic_Sample_Selection_WACV_2024_paper.pdf)] 34 | (**WACV 24**) Effective Restoration of Source Knowledge in Continual Test Time Adaptation [[paper](https://openaccess.thecvf.com/content/WACV2024/html/Niloy_Effective_Restoration_of_Source_Knowledge_in_Continual_Test_Time_Adaptation_WACV_2024_paper.html)] 35 | 36 | 37 | (**ICLR 24**) ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation [[paper](https://arxiv.org/pdf/2306.04344)][[code](https://github.com/Yangsenqiao/vida)] \ 38 | (**ICLR 24**) Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation [[paper](https://arxiv.org/pdf/2401.08328)][[code](https://github.com/devavratTomar/unmixtns)] 39 | 40 | 41 | 42 | (**AAAI 24**) Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization[[paper](https://arxiv.org/pdf/2312.10165)][[code](https://github.com/ynanwu/MABN)] \ 43 | (**AAAI 24**) Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization[[paper](https://arxiv.org/pdf/2309.14949.pdf)][[code](https://github.com/Gorilla-Lab-SCUT/TRIBE)] \ 44 | (**AAAI 24**) Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction[[paper](https://arxiv.org/pdf/2303.09792)][[code](https://github.com/Anonymous-012/SVDP)] 45 | 46 | 47 | 48 | ## 2023 49 | 50 | (**CVPR 23**) EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization [[paper](https://arxiv.org/pdf/2303.01904.pdf)][[code](https://github.com/Lily-Le/EcoTTA)] 51 | (**CVPR 23**) A Probabilistic Framework for Lifelong Test-Time Adaptation [[paper](https://arxiv.org/pdf/2212.09713.pdf)][[code](https://github.com/dhanajitb/petal)] 52 | (**CVPR 23**) Robust Mean Teacher for Continual and Gradual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2211.13081.pdf)][[code](https://github.com/mariodoebler/test-time-adaptation)] 53 | (**CVPR 23**) TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Tomar_TeSLA_Test-Time_Self-Learning_With_Automatic_Adversarial_Augmentation_CVPR_2023_paper.pdf)][[code](https://github.com/devavratTomar/TeSLA)] 54 | 55 | (**ICCV 23**) Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Lee_Towards_Open-Set_Test-Time_Adaptation_Utilizing_the_Wisdom_of_Crowds_in_ICCV_2023_paper.pdf)] \ 56 | (**ICCV 23**) Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Hatem_Point-TTA_Test-Time_Adaptation_for_Point_Cloud_Registration_Using_Multitask_Meta-Auxiliary_ICCV_2023_paper.pdf)] 57 | 58 | (**AAAI 23**) Decorate the Newcomers:Visual Domain Prompt for Continual Test Time Adaptation [[paper](https://arxiv.org/pdf/2212.04145.pdf)] 59 | 60 | (**IJCAI 23**) Exploring Safety Supervision for Continual Test-time Domain Adaptation [[paper](https://www.ijcai.org/proceedings/2023/0183.pdf)] 61 | 62 | 63 | 64 | (**ArXiv 23**) Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation [[paper](https://arxiv.org/pdf/2303.10457.pdf)] 65 | (**ArXiv 23**) ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation [[paper](https://arxiv.org/pdf/2306.04344.pdf)][[code](https://github.com/Yangsenqiao/vida)] 66 | (**ArXiv 23**) Distribution-Aware Continual Test Time Adaptation for Semantic Segmentation [[paper](https://arxiv.org/pdf/2309.13604.pdf)][[code](https://arxiv.org/pdf/2309.13604.pdf)] 67 | (**ArXiv 23**) Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2311.18270.pdf)] 68 | (**ArXiv 23**) Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation [[paper](https://arxiv.org/pdf/2312.12480.pdf)] 69 | 70 | ## 2022 71 | 72 | (**CVPR 22**) Continual Test-Time Domain Adaptation [[paper](https://arxiv.org/pdf/2203.13591.pdf)][[code](https://github.com/qinenergy/cotta)] 73 | (**NeurIPS 22**) NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation [[paper](https://arxiv.org/pdf/2208.05117.pdf)][[code](https://github.com/TaesikGong/NOTE)] 74 | 75 |
76 | 77 | ![visitors](https://visitor-badge.laobi.icu/badge?page_id=SUSI-Lab.750121247&left_color=green&right_color=red) 78 | 79 |
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