Relation-Aware Pedestrian Attribute Recognition with Graph Convolutional Networks


  • Zichang Tan Chinese Academy of Sciences
  • Yang Yang Chinese Academy of Sciences
  • Jun Wan Chinese Academy of Sciences
  • Guodong Guo Baidu Research
  • Stan Z. Li Chinese Academy of Sciences



In this paper, we propose a new end-to-end network, named Joint Learning of Attribute and Contextual relations (JLAC), to solve the task of pedestrian attribute recognition. It includes two novel modules: Attribute Relation Module (ARM) and Contextual Relation Module (CRM). For ARM, we construct an attribute graph with attribute-specific features which are learned by the constrained losses, and further use Graph Convolutional Network (GCN) to explore the correlations among multiple attributes. For CRM, we first propose a graph projection scheme to project the 2-D feature map into a set of nodes from different image regions, and then employ GCN to explore the contextual relations among those regions. Since the relation information in the above two modules is correlated and complementary, we incorporate them into a unified framework to learn both together. Experiments on three benchmarks, including PA-100K, RAP, PETA attribute datasets, demonstrate the effectiveness of the proposed JLAC.




How to Cite

Tan, Z., Yang, Y., Wan, J., Guo, G., & Li, S. Z. (2020). Relation-Aware Pedestrian Attribute Recognition with Graph Convolutional Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12055-12062.



AAAI Technical Track: Vision