  Despite the noticeable progress in perceptual tasks like detection, instance
segmentation and human parsing, computers still perform unsatisfactorily on
visually understanding humans in crowded scenes, such as group behavior
analysis, person re-identification and autonomous driving, etc. To this end,
models need to comprehensively perceive the semantic information and the
differences between instances in a multi-human image, which is recently defined
as the multi-human parsing task. In this paper, we present a new large-scale
database "Multi-Human Parsing (MHP)" for algorithm development and evaluation,
and advances the state-of-the-art in understanding humans in crowded scenes.
MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic
category labels, involving 2-26 persons per image and captured in real-world
scenes from various viewpoints, poses, occlusion, interactions and background.
We further propose a novel deep Nested Adversarial Network (NAN) model for
multi-human parsing. NAN consists of three Generative Adversarial Network
(GAN)-like sub-nets, respectively performing semantic saliency prediction,
instance-agnostic parsing and instance-aware clustering. These sub-nets form a
nested structure and are carefully designed to learn jointly in an end-to-end
way. NAN consistently outperforms existing state-of-the-art solutions on our
MHP and several other datasets, and serves as a strong baseline to drive the
future research for multi-human parsing.
