  Recognizing multiple labels of images is a practical and challenging task,
and significant progress has been made by searching semantic-aware regions and
modeling label dependency. However, current methods cannot locate the semantic
regions accurately due to the lack of part-level supervision or semantic
guidance. Moreover, they cannot fully explore the mutual interactions among the
semantic regions and do not explicitly model the label co-occurrence. To
address these issues, we propose a Semantic-Specific Graph Representation
Learning (SSGRL) framework that consists of two crucial modules: 1) a semantic
decoupling module that incorporates category semantics to guide learning
semantic-specific representations and 2) a semantic interaction module that
correlates these representations with a graph built on the statistical label
co-occurrence and explores their interactions via a graph propagation
mechanism. Extensive experiments on public benchmarks show that our SSGRL
framework outperforms current state-of-the-art methods by a sizable margin,
e.g. with an mAP improvement of 2.5%, 2.6%, 6.7%, and 3.1% on the PASCAL VOC
2007 & 2012, Microsoft-COCO and Visual Genome benchmarks, respectively. Our
codes and models are available at https://github.com/HCPLab-SYSU/SSGRL.
