Hugging Face Trending Papers

Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions

Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data undergoes continual distributional shifts. Continual Test-Time Adaptation (CTTA) addresses this challenge by adapting pretrained models to non-stationary target distributions on-the-fly, without access to source data or labeled targets, while mitigating two critical failure modes: catastrophic forgetting of source knowledge and error accumulation from noisy pseudo-labels over extended time horizons.

arXiv Computer Vision

Aug 24

Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions

The paper surveys Continual Test-Time Adaptation (CTTA), a framework that adapts pretrained computer‑vision models to non‑stationary target distributions without source data or labeled targets, while avoiding catastrophic forgetting and error accumulation. It formally defines the CTTA problem, categorizes existing methods into optimization‑based, parameter‑efficient, and architecture‑based families, and reviews representative techniques and benchmarks across standard evaluation settings. The survey also outlines current limitations and proposes future research directions, such as adapting foundation models and black‑box systems.

By Sarthak Kumar Maharana, Shambhavi Mishra, Yunbei Zhang, Shuaicheng Niu, Taki Hasan Rafi, Jihun Hamm, Marco Pedersoli, Jose Dolz, Yunhui Guo

llms computer-vision benchmarks

arXiv Machine Learning

Jul 20

SloMo-Fast: Slow-Momentum and Fast-Adaptive Teachers for Source-Free Continual Test-Time Adaptation

arXiv:2511. 18468v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) is crucial for deploying models in real-world applications with unseen, evolving target domains.

By Md Akil Raihan Iftee, Mir Sazzat Hossain, Rakibul Hasan Rajib, Tariq Iqbal, Md Mofijul Islam, M Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman

benchmarks safety

arXiv AI

Sep 23

Memory-Free Continual Learning with Null Space Adaptation for Zero-Shot Vision-Language Models

arXiv:2510.21175v2 Announce Type: replace Abstract: Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated remarkable zero-shot generalization, enabling deployment in a wide range...

By Yujin Jo, Taesup Kim

llms efficiency multimodal benchmarks

arXiv Computer Vision

4d ago

Not Every Correction Helps: Gain-Guided Continual Test-Time Adaptation

arXiv:2609.36655v1 Announce Type: new Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods o...

By Youjia Zhang, Huiling Liu, Soyun Choi, Jaehong Yoon, Sungeun Hong

benchmarks

arXiv Machine Learning

Jun 30

Audio-Visual Continual Test-Time Adaptation without Forgetting

arXiv:2602. 18528v2 Announce Type: replace Abstract: Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to poor accuracy.

By Sarthak Kumar Maharana, Akshay Mehra, Bhavya Ramakrishna, Yunhui Guo, Guan-Ming Su

multimodal benchmarks

arXiv AI

Jul 14

Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models

arXiv:2607. 09785v1 Announce Type: cross Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams.

By Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen, Joachim Collin, Bart{\l}omiej Twardowski, Szymon {\L}ukasik, Tinne Tuytelaars

robotics efficiency multimodal benchmarks

arXiv Computer Vision

Sep 10

Distilling Image Prototypes for Guided Test-Time Adaptation

arXiv:2609.09737v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from noi...

By Liwen Wang, Xingbo Dong, Iman Yi Liao, Deyin Liu, Massimo Tistarelli, Lin Yuanbo Wu, Zhe Jin

benchmarks

arXiv Machine Learning

Sep 22

SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

arXiv:2609.24111v1 Announce Type: new Abstract: Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating...

By Muhammad Sudipto Siam Dip, Ali Etemad

llms

arXiv Machine Learning

Aug 21

Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation

arXiv:2608. 19890v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) methods aim to bridge the domain gap between the source and target domains.

By Jia-Qi Lin, Yuangang Pan, Chang-Dong Wang, Haizhang Zhang, Ivor W. Tsang, Joey Tianyi Zhou

fine-tuning benchmarks

arXiv AI

Jun 9

LargeMonitor: Monitoring Online Task-Free Continual Learning via Large Pretrained Models

arXiv:2606. 09430v1 Announce Type: cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints and without any explicit task identifiers.

By Mingqi Yuan, Xiaoquan Sun, Shihao Luo, Jiayu Chen

agents multimodal benchmarks

arXiv Machine Learning

Aug 4

Logit-Origin Centering for Singleton Test-Time Adaptation

arXiv:2608. 01074v1 Announce Type: new Abstract: Tabular data is used extensively in many real-world use cases.

By Mayank Sharma, Rohit Kumar Mourya, Pratik Mazumder

llms computer-vision benchmarks

arXiv Machine Learning

Aug 4

Learning What to Remember: Test-Time Training via Context Distillation

arXiv:2608. 01672v1 Announce Type: cross Abstract: Effective long-context modeling is not merely about retaining more of the past, but about preserving the information that may prove relevant later.

By Zixuan Wang, Xingyu Dang, Rui-Jie Zhu, Zixin Wen, Hengyu Fu, Wenhao Chai, Jason D. Lee

llms efficiency

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