Geometry Sensitive Cross-Modal Reasoning for Composed Query Based Image Retrieval

doi:10.1109/TIP.2021.3138302

	Geometry Sensitive Cross-Modal Reasoning for Composed Query Based Image Retrieval
	Zhang, Feifei 1,2; Xu, Mingliang 3; Xu, Changsheng2,4,5
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2022
卷号	31 页码:1000-1011
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
摘要	Composed Query Based Image Retrieval (CQBIR) aims at retrieving images relevant to a composed query containing a reference image with a requested modification expressed via a textual sentence. Compared with the conventional image retrieval which takes one modality as query to retrieve relevant data of another modality, CQBIR poses great challenge over the semantic gap between the reference image and modification text in the composed query. To solve the challenge, previous methods either resort to feature composition that cannot model interactions in the query or explore inter-modal attention while ignoring the spatial structure and visual-semantic relationship. In this paper, we propose a geometry sensitive cross-modal reasoning network for CQBIR by jointly modeling the geometric information of the image and the visual-semantic relationship between the reference image and modification text in the query. Specifically, it contains two key components: a geometry sensitive inter-modal attention module (GS-IMA) and a text-guided visual reasoning module (TG-VR). The GS-IMA introduces the spatial structure into the inter-modal attention in both implicit and explicit manners. The TG-VR models the unequal semantics not included in the reference image to guide further visual reasoning. As a result, our method can learn effective feature for the composed query which does not exhibit literal alignment. Comprehensive experimental results on three standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art methods.
关键词	Visualization Image retrieval Semantics Cognition Geometry Task analysis Electronic mail Composed query based image retrieval semantic gap spatial structure inter-modal attention text-guided visual reasoning
DOI	10.1109/TIP.2021.3138302
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102200] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[62002355] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[62072455] ; National Natural Science Foundation of China[62102415] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[U1836220] ; Key Research Program of Frontier Sciences of CAS[QYZDJ-SSW-JSC039] ; Beijing Natural Science Foundation[L201001]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Beijing Natural Science Foundation
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000742179600002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	多模态智能
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47049
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Changsheng
作者单位	1.Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300000, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Peng Cheng Lab, Shenzhen 518066, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhang, Feifei,Xu, Mingliang,Xu, Changsheng. Geometry Sensitive Cross-Modal Reasoning for Composed Query Based Image Retrieval[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:1000-1011.
APA	Zhang, Feifei,Xu, Mingliang,&Xu, Changsheng.(2022).Geometry Sensitive Cross-Modal Reasoning for Composed Query Based Image Retrieval.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,1000-1011.
MLA	Zhang, Feifei,et al."Geometry Sensitive Cross-Modal Reasoning for Composed Query Based Image Retrieval".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):1000-1011.