└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Continual Test-Time Adaptation [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) 2 | 3 | :wave: A curated list of awesome Continual Test-Time Adaptation resources. We appreciate everyone's contributions. 4 | 5 | :raised_hands: For the missing paper welcome to submit issue to add! 6 | 7 | - `CoTTA` [Wang et al., CVPR 2022] **Continual Test-Time Domain Adaptation** [[PDF]](https://arxiv.org/abs/2203.13591) [[CODE]](https://github.com/qinenergy/cotta) 8 | 9 | - `MECTA` [Hong et al., ICLR 2023] **MECTA: Memory-economic continual test-time model adaptation** [[PDF]](https://openreview.net/forum?id=N92hjSf5NNh) [[CODE]](https://github.com/SonyResearch/MECTA) 10 | 11 | - `ECoTTA` [Song et al., CVPR 2023] **EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization** [[PDF]](https://openaccess.thecvf.com/content/CVPR2023/html/Song_EcoTTA_Memory-Efficient_Continual_Test-Time_Adaptation_via_Self-Distilled_Regularization_CVPR_2023_paper.html) [[CODE (Community Implementation)]](https://github.com/Lily-Le/EcoTTA) 12 | 13 | - `RMT` [Dobler et al., CVPR 2023] **Robust mean teacher for continual and gradual test-time adaptation** [[PDF]](https://openaccess.thecvf.com/content/CVPR2023/papers/Dobler_Robust_Mean_Teacher_for_Continual_and_Gradual_Test-Time_Adaptation_CVPR_2023_paper.pdf) [[CODE]](https://github.com/mariodoebler/test-time-adaptation) 14 | 15 | - `Note` [Gong et al., CVPR 2023] **Note: Robust continual test-time adaptation against temporal correlation** [[PDF]](https://proceedings.neurips.cc/paper_files/paper/2022/file/ae6c7dbd9429b3a75c41b5fb47e57c9e-Paper-Conference.pdf) [[CODE]](https://github.com/TaesikGong/NOTE) 16 | 17 | - `CoMAC` [Cao et al., ICCV 2023] **Multi-modal continual test-time adaptation for 3D semantic segmentation** 18 | [[PDF]](https://openaccess.thecvf.com/content/ICCV2023/html/Cao_Multi-Modal_Continual_Test-Time_Adaptation_for_3D_Semantic_Segmentation_ICCV_2023_paper.html) [[CODE]](https://sites.google.com/view/mmcotta) 19 | 20 | 21 | - `VDP` [Gan et al., AAAI 2023] **Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation** [[PDF]](https://arxiv.org/abs/2212.04145) 22 | 23 | - `SVDP` [Yang et al., AAAI 2024] **Exploring sparse visual prompt for cross-domain semantic segmentation** [[PDF]](https://arxiv.org/pdf/2303.09792.pdf) [[CODE]](https://github.com/Anonymous-012/SVDP) 24 | 25 | - `ViDA` [Liu et al., ICLR 2024] **Vida: Homeostatic visual domain adapter for continual test time adaptation** [[PDF]](https://arxiv.org/pdf/2306.04344.pdf) [[CODE]](https://github.com/Yangsenqiao/vida) 26 | 27 | - `DAT` [Ni et al., ICRA 2024] **Distribution-aware continual test time adaptation for semantic segmentation** [[PDF]](https://arxiv.org/pdf/2309.13604) [[CODE]](https://github.com/RochelleNi/DAT) 28 | 29 | - `ADMA` [Liu et al., CVPR 2024] **Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation**[[PDF]](https://arxiv.org/abs/2312.12480) 30 | 31 | - `BECoTTA` [Lee et al., Arxiv 2402] **BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation** [[PDF]](https://arxiv.org/abs/2402.08712) [[CODE]](https://github.com/daeunni/becotta) 32 | 33 | - [Yang et al., CVPR 2024] **A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability** 34 | [[PDF]](https://cvpr.thecvf.com/virtual/2024/poster/31036) [[CODE]](https://cvpr.thecvf.com/virtual/2024/poster/31036) 35 | 36 | - `VPTTA` [Chen et al., CVPR 2024] **Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation** 37 | [[PDF]](https://arxiv.org/pdf/2311.18363.pdf) [[CODE]](https://github.com/Chen-Ziyang/VPTTA) --------------------------------------------------------------------------------