2026
Leveraging Evidence Priors for Robust Prompt Learning under Noisy Supervision in Vision-Language Models
ICML 2026poster
Prompt learning for vision-language models (VLMs) often suffers from performance degradation when adapting to downstream tasks with noisy labels. Existing methods that rely on filtering or reconstructing supervision can propagate errors, leading to sharp performance drops. We observe that pre-traine…