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cs.CV2026

Multi-modal Test-time Adaptation via Adaptive Probabilistic Gaussian Calibration

Jinglin Xu, Yi Li, Chuxiong Sun +3

Multi-modal test-time adaptation (TTA) enhances the resilience of benchmark multi-modal models against distribution shifts by leveraging the unlabeled target data during inference.…

cs.CV2026

Test-Time Perturbation Learning with Delayed Feedback for Vision-Language-Action Models

Zehua Zang, Xi Wang, Fuchun Sun +4

Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, such as small changes in object…

cs.CV2026

Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification

Kunlun Xu, Haotong Cheng, Jiangmeng Li +2

Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model. Existing LReID approaches typically focus on learning from…

cs.CV2025

Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification

Kunlun Xu, Fan Zhuo, Jiangmeng Li +2

Current lifelong person re-identification (LReID) methods predominantly rely on fully labeled data streams. However, in real-world scenarios where annotation resources are limited,…

cs.CV2025

BayesTTA: Continual-Temporal Test-Time Adaptation for Vision-Language Models via Gaussian Discriminant Analysis

Shuang Cui, Jinglin Xu, Yi Li +6

Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but degrade significantly under \textit{temporally evolving distribution shifts} common in real-worl…