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

10 Apr 2018Jian ZhaoJianshu LiYu ChengLi ZhouTerence SimShuicheng YanJiashi Feng

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... (read more)

PDF Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Multi-Human Parsing MHP v1.0 NAN AP 0.5 57.09% # 1
Multi-Human Parsing MHP v2.0 NAN AP 0.5 25.14% # 1
Multi-Human Parsing PASCAL-Person-Part NAN AP 0.5 59.70% # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet