Paper Info
Title
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
Abstract
Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated impressive results, their performance highly depends on the quantity and quality of training data. In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations. Our key idea is to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose. Using these estimated results as additional training data, our semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU on the PASCAL-Person-Part dataset [6], and we achieve state-of-the-art human parsing results. Our approach is general and can be readily extended to other object/animal parsing task assuming that their anatomical similarity can be annotated by keypoints. The proposed model and accompanying source code will be made publicly available.
Year
DOI
Venue
2018
10.1109/CVPR.2018.00015
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
FCN,PASCAL-Person-Part dataset,semisupervised model,human keypoint annotations,synthetic human part segmentation data,fully convolutional networks,ground truth segmentations,computer vision tasks,human semantic part segmentation,pose-guided knowledge transfer,semisupervised human body Part parsing,anatomical similarity
Conference
abs/1805.04310
ISSN
ISBN
Citations 
1063-6919
978-1-5386-6421-6
13
PageRank 
References 
Authors
0.56
22
6
Name
Order
Citations
PageRank
Haoshu Fang1576.86
Guansong Lu2151.95
Xiaolin Fang3142.93
Jianwen Xie413316.99
Yu-Wing Tai5130.56
Cewu Lu699362.08