HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding

TL;DR

HGCLIP introduces a graph-based hierarchical framework that enhances vision-language models by better exploiting class hierarchies, leading to improved performance across multiple visual recognition benchmarks.

Contribution

The paper presents HGCLIP, a novel graph-based method that integrates hierarchical class structures into CLIP, improving multi-granularity classification performance.

Findings

Significant improvements on 11 visual recognition benchmarks.

Effective incorporation of hierarchical information into vision-language models.

Enhanced class-aware feature representation through graph encoding.

Abstract

Object categories are typically organized into a multi-granularity taxonomic hierarchy. When classifying categories at different hierarchy levels, traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. Recent studies integrating Vision-Language Models (VLMs) with class hierarchies have shown promise, yet they fall short of fully exploiting the hierarchical relationships. These efforts are constrained by their inability to perform effectively across varied granularity of categories. To tackle this issue, we propose a novel framework (HGCLIP) that effectively combines CLIP with a deeper exploitation of the Hierarchical class structure via Graph representation learning. We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category. After passing through a graph encoder, the textual features incorporate hierarchical structure information, while the image features emphasize class-aware features derived from prototypes through the attention mechanism. Our approach demonstrates significant improvements on 11 diverse visual recognition benchmarks. Our codes are fully available at https://github.com/richard-peng-xia/HGCLIP.