Progressive Cognitive Human Parsing

Human parsing is an important task for human-centric understanding. Generally, two mainstreams are used to deal with this challenging and fundamental problem. The first one is employing extra human pose information to generate hierarchical parse graph to deal with human parsing task. Another one is training an end-to-end network with the semantic information in image level. In this paper, we develop an end-to-end progressive cognitive network to segment human parts. In order to establish a hierarchical relationship, a novel component-aware region convolution structure is proposed. With this structure, latter layers inherit prior component information from former layers and pay its attention to a finer component. In this way, we deal with human parsing as a progressive recognition task, that is, we first locate the whole human and then segment the hierarchical components gradually. The experiments indicate that our method has a better location capacity for the small objects and a better classification capacity for the large objects. Moreover, our framework can be embedded into any fully convolutional network to enhance the performance significantly.

Authors

• Bingke Zhu Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China Objecteye Inc., Beijing, China
• Yingying Chen Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China Objecteye Inc., Beijing, China
• Ming Tang Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
• Jinqiao Wang Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences Wuhan AI Research

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