A top-down unified framework for instance-level human parsing

Haifang Qin, Weixiang Hong, Wei Chih Hung, Yi Hsuan Tsai, Ming Hsuan Yang

Research output: Contribution to conferencePaper

Abstract

Instance-level human parsing is one of the essential tasks for human-centric analysis which aims to segment various body parts and associate each part with the corresponding human instance simultaneously. Most state-of-the-art methods group instances upon multi-human parsing results, but they tend to miss instances and fail in grouping under the crowded scene. To address this problem, we propose a top-down unified framework to simultaneously detect human instance and parse every part within that instance. To better parse the single human, we also design an attention module, which is aggregated to our parsing network. As a result, our approach is capable of obtaining fine-grained parsing results and the corresponding human mask in a single forward pass. Experiments show that the proposed algorithm performs favorably against state-of-the-art methods on the CIHP and PASCAL-Person-Part datasets.

Original languageEnglish
Publication statusPublished - 2020
Event30th British Machine Vision Conference, BMVC 2019 - Cardiff, United Kingdom
Duration: 2019 Sep 92019 Sep 12

Conference

Conference30th British Machine Vision Conference, BMVC 2019
CountryUnited Kingdom
CityCardiff
Period19/9/919/9/12

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Qin, H., Hong, W., Hung, W. C., Tsai, Y. H., & Yang, M. H. (2020). A top-down unified framework for instance-level human parsing. Paper presented at 30th British Machine Vision Conference, BMVC 2019, Cardiff, United Kingdom.