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

• URL: http://arxiv.org/abs/2311.14064v2
• Date: Thu, 14 Mar 2024 13:03:53 GMT
• Title: HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
• Authors: Peng Xia, Xingtong Yu, Ming Hu, Lie Ju, Zhiyong Wang, Peibo Duan, Zongyuan Ge,
• Abstract summary: 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. We propose a novel framework that effectively combines CLIP with a deeper exploitation of the Hierarchical class structure via Graph representation learning.
• Score: 18.95003393925676
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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