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Despite the noticeable progress in perceptual tasks like detection, instancesegmentation and human parsing, computers still perform unsatisfactorily onvisually understanding humans in crowded scenes, such as group behavioranalysis, person re-identification and autonomous driving, etc. To this end,models need to comprehensively perceive the semantic information and thedifferences between instances in a multi-human image, which is recently definedas the multi-human parsing task. In this paper, we present a new large-scaledatabase "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 semanticcategory labels, involving 2-26 persons per image and captured in real-worldscenes from various viewpoints, poses, occlusion, interactions and background.We further propose a novel deep Nested Adversarial Network (NAN) model formulti-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 anested structure and are carefully designed to learn jointly in an end-to-endway. NAN consistently outperforms existing state-of-the-art solutions on ourMHP and several other datasets, and serves as a strong baseline to drive thefuture research for multi-human parsing.

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing