Devil in the Details: Towards Accurate Single and Multiple Human Parsing

  • Tao Ruan Beijjing Jiaotong University
  • Ting Liu Beijing Jiaotong University
  • Zilong Huang Huazhong University of Science and Technology
  • Yunchao Wei University of Illinois, Urbana Champaign
  • Shikui Wei Beijing Jiaotong University
  • Yao Zhao Beijing Jiaotong University

Abstract

Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task. The advantages of these useful properties finally result in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end trainable and can be easily adopted for conducting multiple human parsing. Benefiting the superiority of CE2P, we won the 1st places on all three human parsing tracks in the 2nd Look into Person (LIP) Challenge. Without any bells and whistles, we achieved 56.50% (mIoU), 45.31% (mean APr) and 33.34% (APp0.5) in Track 1, Track 2 and Track 5, which outperform the state-of-the-arts more than 2.06%, 3.81% and 1.87%, respectively. We hope our CE2P will serve as a solid baseline and help ease future research in single/multiple human parsing. Code has been made available at https://github.com/liutinglt/CE2P.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning