A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition

• URL: http://arxiv.org/abs/2503.22079v1
• Date: Fri, 28 Mar 2025 01:55:43 GMT
• Title: A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition
• Authors: Kunshan Yang, Wenwei Luo, Yuguo Hu, Jiafu Yan, Mengmeng Jing, Lin Zuo,
• Abstract summary: We propose a semantic-enhanced heterogeneous graph learning method for flexible objects recognition.<n>An adaptive scanning module is employed to extract semantic context, facilitating the matching of flexible objects with varying shapes and sizes.<n>A graph generation module aggregates global visual and local semantic node features, improving the recognition of flexible objects.
• Score: 3.5624857747396814
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based models, such as graph convolution networks and graph vision models, are promising in flexible objects recognition due to their ability of capturing variable relations within the flexible objects. These methods, however, often focus on global visual relationships or fail to align semantic and visual information. To alleviate these limitations, we propose a semantic-enhanced heterogeneous graph learning method. First, an adaptive scanning module is employed to extract discriminative semantic context, facilitating the matching of flexible objects with varying shapes and sizes while aligning semantic and visual nodes to enhance cross-modal feature correlation. Second, a heterogeneous graph generation module aggregates global visual and local semantic node features, improving the recognition of flexible objects. Additionally, We introduce the FSCW, a large-scale flexible dataset curated from existing sources. We validate our method through extensive experiments on flexible datasets (FDA and FSCW), and challenge benchmarks (CIFAR-100 and ImageNet-Hard), demonstrating competitive performance.

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