Abstract. 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