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.
Abstract (translated)
灵活物体识别仍然面临重大挑战，原因在于其形状和大小的多样性、半透明属性以及细微的类别间差异。基于图的方法（如图卷积网络和图形视觉模型）在灵活物体识别中展现出巨大潜力，因为它们能够捕捉到灵活物体内部可变的关系。然而，这些方法通常侧重于全局视觉关系或无法对齐语义与视觉信息。为了解决这些问题，我们提出了一种增强的异构图学习方法。 首先，采用了一个自适应扫描模块来提取具有判别性的语义上下文，这有助于匹配形状和大小各异的灵活物体，并通过对齐语义和视觉节点来加强跨模态特征的相关性。其次，通过一个异构图生成模块聚合全局视觉与局部语义节点特征，从而提高对灵活物体的识别能力。 此外，我们引入了一个名为FSCW的大规模灵活数据集，该数据集是从现有来源汇编而来的。我们在灵活数据集（FDA和FSCW）以及具有挑战性的基准测试（CIFAR-100和ImageNet-Hard）上通过广泛的实验验证了我们的方法的有效性，并展示了其在性能上的竞争力。
URL
https://arxiv.org/abs/2503.22079
