Vision Graph Prompting via Semantic Low-Rank Decomposition

Zixiang Ai; Zichen Liu; Jiahuan Zhou

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arXiv · 2505.04121

Vision Graph Prompting via Semantic Low-Rank Decomposition

Zixiang Ai·Zichen Liu·Jiahuan Zhou

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Abstract

Vision GNN (ViG) demonstrates superior performance by representing images as graph structures, providing a more natural way to capture irregular semantic patterns beyond traditional grid or sequence-based representations. To efficiently adapt ViG to downstream tasks, parameter-efficient fine-tuning techniques like visual prompting become increasingly essential. However, existing prompting methods are primarily designed for Transformer-based models, neglecting the rich topological relationships among nodes and edges in graph-based representations, limiting their capacity to model complex semantics. In this paper, we propose Vision Graph Prompting (VGP), a novel framework tailored for vision graph structures. Our core insight reveals that semantically connected components in the graph exhibit low-rank properties. Building on this observation, we introduce a semantic low-rank prompting method that decomposes low-rank semantic features and integrates them with prompts on vision graph topologies, capturing both global structural patterns and fine-grained semantic dependencies. Extensive experiments demonstrate our method significantly improves ViG's transfer performance on diverse downstream tasks, achieving results comparable to full fine-tuning while maintaining parameter efficiency. Our code is available at https://github.com/zhoujiahuan1991/ICML2025-VGP.

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BibTeX RIS

Zixiang Ai, Zichen Liu, Jiahuan Zhou. 2025-05-24. Vision Graph Prompting via Semantic Low-Rank Decomposition. https://arxiv.org/abs/2505.04121

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