Comprehensive Attribute Prediction Learning for Person Search by Language

doi:10.1109/TIP.2024.3372832

	Comprehensive Attribute Prediction Learning for Person Search by Language
	Niu, Kai 1,2; Huang, Linjiang 3; Long, Yuzhou 1; Huang, Yan 4; Wang, Liang ; Zhang, Yanning 1
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2024
卷号	33 页码:1990-2003
关键词	Person search by language cross-modal retrieval smart video surveillance attribute prediction
ISSN号	1057-7149
DOI	10.1109/TIP.2024.3372832
通讯作者	Niu, Kai(kai.niu@nwpu.edu.cn) ; Huang, Linjiang(ljhuang524@gmail.com)
英文摘要	Person search by language refers to searching for the interested pedestrian images given natural language sentences, which requires capturing fine-grained differences to accurately distinguish different pedestrians, while still far from being well addressed by most of the current solutions. In this paper, we propose the Comprehensive Attribute Prediction Learning (CAPL) method, which explicitly carries out attribute prediction learning, for improving the modeling capabilities of fine-grained semantic attributes and obtaining more discriminative visual and textual representations. First, we construct the semantic ATTribute Vocabulary (ATT-Vocab) based on sentence analysis. Second, the complementary context-wise and attribute-wise attribute predictions are simultaneously conducted to better model the high-frequency in-vocab attributes in our In-vocab Attribute Prediction (IAP) module. Third, to additionally consider the out-of-vocab semantics, we present the Attribute Completeness Learning (ACL) module for better capturing the low-frequency attributes outside the ATT-Vocab, obtaining more comprehensive representations. Combining the IAP and ACL modules together, our CAPL method has obtained the currently state-of-the-art retrieval performance on two widely-used benchmarks, i.e., CUHK-PEDES and ICFG-PEDES datasets. Extensive experiments and analyses have been carried out to validate the effectiveness and generalization capacities of our CAPL method.
资助项目	National Natural Science Foundation of China
WOS关键词	ATTENTION
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001188332200005
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58055]
专题	中国科学院自动化研究所
通讯作者	Niu, Kai; Huang, Linjiang
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci, Natl Engn Lab Integrated Aerosp Ground Ocean Big D, Xian 710072, Peoples R China 2.Northwestern Polytech Univ Shenzhen, Res & Dev Inst, Shenzhen 518063, Peoples R China 3.Chinese Univ Hong Kong, Multimedia Lab, Hong Kong, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Niu, Kai,Huang, Linjiang,Long, Yuzhou,et al. Comprehensive Attribute Prediction Learning for Person Search by Language[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2024,33:1990-2003.
APA	Niu, Kai,Huang, Linjiang,Long, Yuzhou,Huang, Yan,Wang, Liang,&Zhang, Yanning.(2024).Comprehensive Attribute Prediction Learning for Person Search by Language.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,1990-2003.
MLA	Niu, Kai,et al."Comprehensive Attribute Prediction Learning for Person Search by Language".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):1990-2003.