AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning

arXiv:2404.08958v1 Announce Type: cross
Abstract: Recently, pre-trained vision-language models (e.g., CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made to improve few-shot ability of CLIP, key factors on the effectiveness of existing methods have not been well studied, limiting further exploration of CLIP's potential in few-shot learning. In this paper, we first introduce a unified formulation to analyze CLIP-based few-shot learning methods from a perspective of logit bias, which …

abstract arxiv bias clip cs.cl cs.cv cs.lg exploration few-shot few-shot learning key language language models research type vision vision-language models