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

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 uni-fied 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 per-formance 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 per-formance 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 clas-sifier with Multi-branch training. Finally, an Uncertainty-based fusion is developed to incorporate logit bias into CLIP for few-shot classification. The experiments are con-ducted on several widely used benchmarks, and the re-sults show AMU-Tuning clearly outperforms its counter-parts while achieving state-of-the-art performance of CLIP-based few-shot learning without bells and whistles.