Learning Visual Proxy for Compositional Zero-Shot Learning

Shiyu Zhang; Cheng Yan; Yang Liu; Chenchen Jing; Lei Zhou; Wenjun Wang

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arXiv · 2501.13859

Learning Visual Proxy for Compositional Zero-Shot Learning

Shiyu Zhang·Cheng Yan·Yang Liu·Chenchen Jing·Lei Zhou·Wenjun Wang

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Abstract

Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions by leveraging knowledge from seen compositions. Current methods align textual prototypes with visual features via Vision-Language Models (VLMs), but suffer from two limitations: (1) modality gaps hinder the discrimination of semantically similar pairs, and (2) single-modal textual prototypes lack fine-grained visual cues. In this paper, we introduce Visual Proxy Learning, a method that reduces modality gaps and enhances compositional generalization. We initialize visual proxies for attributes, objects, and their compositions using text representations and optimize the visual space to capture fine-grained cues, improving visual representations. Additionally, we propose Cross-Modal Joint Learning (CMJL), which imposes cross-modal constraints between the text-image and fine-grained visual spaces, improving generalization for unseen compositions and discriminating similar pairs. Experiments show state-of-the-art performance in closed-world scenarios and competitive results in open-world settings across four CZSL benchmarks, demonstrating the effectiveness of our approach in compositional generalization.

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Shiyu Zhang, Cheng Yan, Yang Liu, Chenchen Jing, Lei Zhou, Wenjun Wang. 2025-01-23. Learning Visual Proxy for Compositional Zero-Shot Learning. https://arxiv.org/abs/2501.13859

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