Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
Jiuhang Wang, Yuewen Zhang, Tianyu Wang, Hongying Tang, Baoqing Li · Applied Sciences · 2025
As a fundamental upstream task, multi-label image classification (MLIC) work has made a great deal of progress in recent years. Establishing dependencies between targets is crucial for MLIC as targets in the real world always co-occur simultaneously. However, due to the complex spatial relationships and semantic relationships among targets, existing methods fail to effectively establish the dependencies between targets. In this paper, we propose a Two-Dimensional Dependency Model (TDDM) for MLIC. The network consists of an Spatial Feature Dependency Module (SFDM) and a Label Semantic Dependency Module (LSDM), which establish effective dependencies in the dimensions of image spatial features and label semantics, respectively. Our method was tested on three publicly available multi-label image datasets, PASCAL VOC 2007, PASCAL VOC 2012, and MS-COCO, and it produced superior results compared to existing state-of-the-art methods, as demonstrated in our experiments.