AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
Authors: Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei Zhu, Qinghua Hu

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 encourages us to learn an effective logit bias for further improving performance of CLIP-based few-shot learning methods. To this end we disassemble three key components involved in computation of logit bias (i.e. logit features logit predictor and logit fusion) and empirically analyze the effect on performance of few-shot classification. Based on analysis of key components this paper proposes a novel AMU-Tuning method to learn effective logit bias for CLIP-based few-shot classification. Specifically our AMU-Tuning predicts logit bias by exploiting the appropriate Auxiliary features which are fed into an efficient feature-initialized linear classifier with Multi-branch training. Finally an Uncertainty-based fusion is developed to incorporate logit bias into CLIP for few-shot classification. The experiments are conducted on several widely used benchmarks and the results show AMU-Tuning clearly outperforms its counterparts while achieving state-of-the-art performance of CLIP-based few-shot learning without bells and whistles.