Quality-Aware Network for Human Parsing

doi:10.1109/TMM.2022.3217413

CORC > 自动化研究所 > 中国科学院自动化研究所 > 紫东太初大模型研究中心

	Quality-Aware Network for Human Parsing
	Yang, Lu 1; Song, Qing 1; Wang, Zhihui 1; Liu, Zhiwei 2; Xu, Songcen 3; Li, Zhihao 3
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2023
卷号	25 页码:7128-7138
关键词	Computer vision image segmentation multi-media computing
ISSN号	1520-9210
DOI	10.1109/TMM.2022.3217413
通讯作者	Song, Qing(priv@bupt.edu.cn)
英文摘要	How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing. To solve this problem, this work proposes a statistical method based on the output probability map to calculate the pixel classification quality, which is called pixel score. In addition, the Quality-Aware Module (QAM) is proposed to fuse the different quality information, the purpose of which is to estimate the quality of human parsing results. We combine QAM with a concise and effective network design to propose Quality-Aware Network (QANet) for human parsing. Benefiting from the superiority of QAM and QANet, we achieve the best performance on three multiple and one single human parsing benchmarks, including CIHP, MHP-v2, Pascal-Person-Part, ATR and LIP. Without increasing the training and inference time, QAM improves the AP$<^>\text{r}$ criterion by more than 10 points in the multiple human parsing task. QAM can be extended to other tasks with good quality estimation, e.g instance segmentation. Specifically, QAM improves Mask R-CNN by similar to% mAP on COCO and LVISv1.0 datasets. Based on the proposed QAM and QANet, our overall system wins 1st place in CVPR2021 L2ID High-resolution Human Parsing (HRHP) Challenge, and 2nd in CVPR2021 PIC Short-video Face Parsing (SFP) Challenge. Code and models are available at https://github.com/soeaver/QANet.
资助项目	China Postdoctoral Science Foundation[2022M710467] ; National Key Research and Development Program of China[2021YFF0500900]
WOS关键词	POSE
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001102654000030
资助机构	China Postdoctoral Science Foundation ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55162]
专题	紫东太初大模型研究中心
通讯作者	Song, Qing
作者单位	1.Beijing Univ Posts & Telecommun, Beijing 100088, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Huawei Technol, Noahs Ark Lab, Shenzhen 518129, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lu,Song, Qing,Wang, Zhihui,et al. Quality-Aware Network for Human Parsing[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:7128-7138.
APA	Yang, Lu,Song, Qing,Wang, Zhihui,Liu, Zhiwei,Xu, Songcen,&Li, Zhihao.(2023).Quality-Aware Network for Human Parsing.IEEE TRANSACTIONS ON MULTIMEDIA,25,7128-7138.
MLA	Yang, Lu,et al."Quality-Aware Network for Human Parsing".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):7128-7138.