Paper Info
Title
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing.
Abstract
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.
Year
DOI
Venue
2018
10.1145/3240508.3240509
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018
Keywords
DocType
Volume
Multi-Human Parsing, Human Parsing, Nested Adversarial Learning, Generative Adversarial Networks
Conference
abs/1804.03287
ISBN
Citations 
PageRank 
978-1-4503-5665-7
15
0.63
References 
Authors
31
7
Name
Order
Citations
PageRank
Jian Zhao1595.07
Jianshu Li214112.04
Yu Cheng361555.76
Li Zhou4241.43
Terence Sim52562169.42
Shuicheng Yan69701359.54
Jiashi Feng72165140.81