Mutual Prompt Leaning for Vision Language Models

Large pre-trained vision language models (VLMs) have demonstrated impressive representation learning capabilities, but their transferability across various downstream tasks heavily relies on prompt learning. Since VLMs consist of text and visual sub-branches, existing prompt approaches are mainly divided into text and visual prompts. Recent text prompt methods have achieved great performance by designing input-condition prompts that encompass both text and image domain knowledge. However, roughly incorporating the same image feature into each learnable text token may be unjustifiable, as it could result in learnable text prompts being concentrated on one or a subset of characteristics. In light of this, we propose a fine-grained text prompt (FTP) that decomposes the single global image features into several finer-grained semantics and incorporates them into corresponding text prompt tokens. On the other hand, current methods neglect valuable text semantic information when building the visual prompt. Furthermore, text information contains redundant and negative category semantics. To address this, we propose a text-reorganized visual prompt (TVP) that reorganizes the text descriptions of the current image to construct the visual prompt, guiding the image branch to attend to class-related representations. By leveraging both FTP and TVP, we enable mutual prompting between the text and visual modalities, unleashing their potential to tap into the representation capabilities of VLMs. Extensive experiments on 11 classification benchmarks show that our method surpasses existing methods by a large margin. In particular, our approach improves recent state-of-the-art CoCoOp by 4.79% on new classes and 3.88% on harmonic mean over eleven classification benchmarks.