Macro-Micro Adversarial Network for Human Parsing

  • Yawei LuoEmail author
  • Zhedong Zheng
  • Liang Zheng
  • Tao Guan
  • Junqing Yu
  • Yi Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)


In human parsing, the pixel-wise classification loss has drawbacks in its low-level local inconsistency and high-level semantic inconsistency. The introduction of the adversarial network tackles the two problems using a single discriminator. However, the two types of parsing inconsistency are generated by distinct mechanisms, so it is difficult for a single discriminator to solve them both. To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN). It has two discriminators. One discriminator, Macro D, acts on the low-resolution label map and penalizes semantic inconsistency, e.g., misplaced body parts. The other discriminator, Micro D, focuses on multiple patches of the high-resolution label map to address the local inconsistency, e.g., blur and hole. Compared with traditional adversarial networks, MMAN not only enforces local and semantic consistency explicitly, but also avoids the poor convergence problem of adversarial networks when handling high resolution images. In our experiment, we validate that the two discriminators are complementary to each other in improving the human parsing accuracy. The proposed framework is capable of producing competitive parsing performance compared with the state-of-the-art methods, i.e., mIoU = 46.81% and 59.91% on LIP and PASCAL-Person-Part, respectively. On a relatively small dataset PPSS, our pre-trained model demonstrates impressive generalization ability. The code is publicly available at


Human parsing Adversarial network Inconsistency Macro-Micro 



This work is partially supported by the National Natural Science Foundation of China (No. 61572211). We acknowledge the Data to Decisions CRC (D2D CRC) and the Cooperative Research Centers Programme for funding this research.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yawei Luo
    • 1
    • 2
    Email author
  • Zhedong Zheng
    • 2
  • Liang Zheng
    • 2
    • 3
  • Tao Guan
    • 1
  • Junqing Yu
    • 1
  • Yi Yang
    • 2
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.CAI, University of Technology SydneySydneyAustralia
  3. 3.Singapore University of Technology and DesignSingaporeSingapore

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