用图注意力网络构建图像代表模型，实现更精准的分类与检索。
Image Categorization and Search via a GAT Autoencoder and Representative Models
- 以图像代表模型为核心，通过图结构捕捉相似关系
- 在标准数据集上分类与检索准确率优于传统特征方法
- 适合需要语义理解的图像搜索场景
我们提出一种基于图注意力网络（GAT）自编码器的图像分类与检索方法。该方法以代表性模型为中心，构建图像及其类别代表模型。图中节点代表图像或其代表，边表示相似性关系。GAT突出重要特征与图像间关系，使自编码器生成上下文感知的潜在表示，反映图像与其邻近图像的关键特征。从这些嵌入中提取类别代表，并通过比较查询图像代表与类别代表完成分类。随后，在识别出的类别内检索最相似图像。实验表明，该代表性中心方法在标准特征技术基础上显著提升性能。
原文摘要 · Abstract (English)
We propose a method for image categorization and retrieval that leverages graphs and a graph attention network (GAT)-based autoencoder. Our approach is representative-centric, that is, we execute the categorization and retrieval process via the representative models we construct for the images and image categories. We utilize a graph where nodes represent images (or their representatives) and edges capture similarity relationships. GAT highlights important features and relationships between images, enabling the autoencoder to construct context-aware latent representations that capture the key features of each image relative to its neighbors. We obtain category representatives from these embeddings and categorize a query image by comparing its representative to the category representatives. We then retrieve the most similar image to the query image within its identified category. We demonstrate the effectiveness of our representative-centric approach through experiments with both the GAT autoencoders and standard feature-based techniques.
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