Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP

• URL: http://arxiv.org/abs/2412.11375v1
• Date: Mon, 16 Dec 2024 02:03:45 GMT
• Title: Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
• Authors: Yayuan Li, Jintao Guo, Lei Qi, Wenbin Li, Yinghuan Shi,
• Abstract summary: We build a mutual guidance mechanism, that introduces an Image-Guided-Text (IGT) component and a Text-Guided-Image (TGI) component. Extensive experiments show that TIMO significantly outperforms the state-of-the-art (SOTA) training-free method. We propose an enhanced variant, TIMO-S, which even surpasses the best training-required methods by 0.33% with approximately 100 times less time cost.
• Score: 22.33658954569737
• License:
• Abstract: Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independently, leading to two essential issues: 1) severe anomalous match in image modality; 2) varying quality of generated text prompts. To address these issues, we build a mutual guidance mechanism, that introduces an Image-Guided-Text (IGT) component to rectify varying quality of text prompts through image representations, and a Text-Guided-Image (TGI) component to mitigate the anomalous match of image modality through text representations. By integrating IGT and TGI, we adopt a perspective of Text-Image Mutual guidance Optimization, proposing TIMO. Extensive experiments show that TIMO significantly outperforms the state-of-the-art (SOTA) training-free method. Additionally, by exploring the extent of mutual guidance, we propose an enhanced variant, TIMO-S, which even surpasses the best training-required methods by 0.33% with approximately 100 times less time cost. Our code is available at https://github.com/lyymuwu/TIMO.

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