Selective Vision-Language Subspace Projection for Few-shot CLIP

TL;DR

This paper introduces SSP, a training-free method that improves image-text alignment in CLIP by using local features to project data into subspaces, significantly enhancing few-shot classification performance.

Contribution

The paper proposes a novel, training-free subspace projection method that better aligns image and text features in CLIP for few-shot learning.

Findings

Outperforms state-of-the-art alignment methods on 11 datasets

Enhances the quality of image-text feature alignment

Seamlessly integrates into existing CLIP-based frameworks

Abstract

Vision-language models such as CLIP are capable of mapping the different modality data into a unified feature space, enabling zero/few-shot inference by measuring the similarity of given images and texts. However, most existing methods overlook modality gaps in CLIP's encoded features, which is shown as the text and image features lie far apart from each other, resulting in limited classification performance. To tackle this issue, we introduce a method called Selective Vision-Language Subspace Projection (SSP), which incorporates local image features and utilizes them as a bridge to enhance the alignment between image-text pairs. Specifically, our SSP framework comprises two parallel modules: a vision projector and a language projector. Both projectors utilize local image features to span the respective subspaces for image and texts, thereby projecting the image and text features into their respective subspaces to achieve alignment. Moreover, our approach entails only training-free matrix calculations and can be seamlessly integrated into advanced CLIP-based few-shot learning frameworks. Extensive experiments on 11 datasets have demonstrated SSP's superior text-image alignment capabilities, outperforming the state-of-the-art alignment methods. The code is available at https://github.com/zhuhsingyuu/SSP