  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
https://github.com/RoyalVane/MMAN.
