Feiyang Ning

Mitigating Endogenous Confirmation Bias in Noisy Label Learning for Vision-Language Models

Pretrained vision-language models (VLMs), especially CLIP, excel at adapting to downstream tasks through fine-tuning with sufficient high-quality labeled data. However, real-world training data often contains noisy labels, leading to significant performance degradation when models are naively fine-