arXiv:2607. 03900v1 Announce Type: cross Abstract: Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.
By Siru Jiang, Jian Liang, Ran He, Tieniu Tan
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
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
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:2606. 06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations.
By Sunoh Kim, Daeho Um
arXiv:2605. 03403v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) has recently shown strong performance in post-training large language models and vision-language models.
By Yujun Li, Hongyuan Zhang, Yuan Yuan