The paper presents Test-Time Adaptation via Cache Personalization (TTA‑CaP), a gradient‑free, cache‑based method that personalizes vision‑language models for facial expression recognition in videos. TTA‑CaP uses three complementary caches—a personalized static cache, a positive target cache, and a negative target cache—controlled by a tri‑gate mechanism to prevent corruption and provide robust subject‑matched evidence. Experiments on BioVid, StressID, and BAH datasets show that TTA‑CaP outperforms state‑of‑the‑art test‑time adaptation methods while keeping computational and memory overhead low.
By Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi, Alessandro Lameiras Koerich, Marco Pedersoli, Eric Granger
arXiv:2610.06932v1 Announce Type: new
Abstract: Cache-based test-time adaptation (TTA) for vision-language models is often hindered by background bias in global representations and unreliable entropy...
By Siyu Huang, Yueyong Chen, Xuejiao Li, Jun Zhou
arXiv:2608.29395v1 Announce Type: new
Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e
arXiv:2610.19892v1 Announce Type: new
Abstract: Vision-language models suffer significant accuracy drops under common corruptions. While test-time adaptation (TTA) successfully mitigates these shifts...
By Miko{\l}aj Pi\'orczy\'nski, Wojciech Masarczyk, Damian S\'ojka, Sebastian Cygert, Bart{\l}omiej Twardowski
arXiv:2606. 28719v1 Announce Type: new Abstract: Test-time adaptation (TTA) of vision-language models (VLMs) is essential for their robust deployment in dynamic, real-world environments.
By Guanglong Sun, Shuang Cui, Bo Lei, Liyuan Wang, Zihan Zhai, Hongwei Yan, Hang Su, Jun Zhu, Yi Zhong
arXiv:2602.06652v2 Announce Type: replace
Abstract: The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions r...
By Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena, Aryo Pradipta Gema, Wai-Chung Kwan, Fazl Barez, Maria Sofia Bucarelli, Fabrizio Silvestri, Pasquale Minervini
arXiv:2606. 14299v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain vulnerable to distribution shifts encountered at deployment.
By Jiazhen Huang, Xiao Chen, Zhiming Liu, Yaru Sun, Jingyan Jiang, Zhi Wang
The paper introduces selective adaptation for vision‑language models, questioning whether test‑time adaptation (TTA) should always be applied. By analyzing per‑sample predictions before and after adaptation, the authors find that many adaptations are negligible or even harmful, flipping correct predictions. They propose Cross‑Augmentation Similarity (CAS), which skips adaptation when predictions across augmented views are highly similar, achieving comparable or better accuracy while reducing adaptation by up to 85%.
By Siru Jiang, Yuwei Liang, Jian Liang, Ran He, Tieniu Tan
The paper introduces MASA, a method for Wild Test-Time Adaptation that uses a frozen multimodal large language model to provide structured semantic anchors, thereby avoiding the self-referential loop common in existing WTTA techniques. MASA selects a small, diverse set of reliability-ranked anchors, encodes their descriptions, propagates them to nearby test samples, and stores this visual‑semantic information in an online prototype memory. The stored descriptors enable lightweight adaptation of normalization parameters, and MASA is evaluated on the WTTA ImageNet‑C benchmark with ResNet and ViT backbones under limited‑batch, mixed‑domain, and imbalanced‑label‑shift scenarios.
By Zhenbin Wang, Lei Zhang, Lituan Wang, Yan Wang, Zhao Zhang, Wei Huang
The paper introduces SubTTA, a test-time adaptation method for vision‑language models that aligns the semantic subspaces of visual and textual modalities to improve zero‑shot predictions. It addresses two issues: the modality gap caused by distribution shifts and visual nuisance that masks task‑specific semantics. By minimizing chordal distance between principal subspaces and projecting visual features onto a task‑specific textual subspace, SubTTA refines decision boundaries and achieves an average 2.24% improvement over existing TTA methods.
By Zhichen Zeng, Wenxuan Bao, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Xuying Ning, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, Hanghang Tong
arXiv:2607. 22705v1 Announce Type: cross Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations.
By Anuraag Gadehothur Karnam, Tarunesh Sathish
arXiv:2607. 02551v1 Announce Type: cross Abstract: Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal perception.
By Yankai Yang, Yancheng Long, Bin Wen, Fan Yang, Tingting Gao, Han Li, Shuo Yang