Grapy-ML: Graph Pyramid Mutual Learning for Cross-Dataset Human Parsing


  • Haoyu He The University of Sydney
  • Jing Zhang The University of Sydney
  • Qiming Zhang The University of Sydney
  • Dacheng Tao The University of Sydney



Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. In this paper, we propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the cross-dataset human parsing problem, where the annotations are at different granularities. Starting from the prior knowledge of the human body hierarchical structure, we devise a graph pyramid module (GPM) by stacking three levels of graph structures from coarse granularity to fine granularity subsequently. At each level, GPM utilizes the self-attention mechanism to model the correlations between context nodes. Then, it adopts a top-down mechanism to progressively refine the hierarchical features through all the levels. GPM also enables efficient mutual learning. Specifically, the network weights of the first two levels are shared to exchange the learned coarse-granularity information across different datasets. By making use of the multi-granularity labels, Grapy-ML learns a more discriminative feature representation and achieves state-of-the-art performance, which is demonstrated by extensive experiments on the three popular benchmarks, e.g. CIHP dataset. The source code is publicly available at




How to Cite

He, H., Zhang, J., Zhang, Q., & Tao, D. (2020). Grapy-ML: Graph Pyramid Mutual Learning for Cross-Dataset Human Parsing. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 10949-10956.



AAAI Technical Track: Vision