3 papers
cs.LG2025
Panda: Test-Time Adaptation with Negative Data Augmentation
Ruxi Deng, Wenxuan Bao, Tianxin Wei +1
Pretrained VLMs exhibit strong zero-shot classification capabilities, but their predictions degrade significantly under common image corruptions. To improve robustness, many test-t…
cs.CV2025
Mint: A Simple Test-Time Adaptation of Vision-Language Models against Common Corruptions
Wenxuan Bao, Ruxi Deng, Jingrui He
Pretrained vision-language models such as CLIP achieve strong zero-shot generalization but remain vulnerable to distribution shifts caused by input corruptions. In this work, we in…
cs.LG2025
Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning
Wenxuan Bao, Ruxi Deng, Ruizhong Qiu +3
Test-time adaptation with pre-trained vision-language models has gained increasing attention for addressing distribution shifts during testing. Among these approaches, memory-based…