3 accepted papers
Training-free test-time adaptation (TTA) for vision-language models (VLMs) can boost zero-shot classification under mild shifts but often collapses under severe environment/style shifts. We identify two shared failure modes: (i) retrieval confounding, where feature similarity is dominated by style a…
Chain-of-Thought (CoT) prompting enables multi-step reasoning in large language models, yet long-horizon generation remains brittle under distribution shift and context interference: irrelevant cues persist, small deviations compound into inference drift, and late-stage corrections can destabilize t…
Pretrained vision-language models (VLMs), such as CLIP, have shown promising zero-shot out-of-distribution (OOD) detection capabilities by leveraging semantic similarities between input images and textual labels. However, most existing approaches focus solely on expanding the label space in the text…