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

文献类型：期刊论文

作者Zhang, Feifei1,3; Xu, Mingliang2; Xu, Changsheng1,4,5
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2022
卷号31页码:1000-1011
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
ISSN号1057-7149
DOI10.1109/TIP.2021.3138302
通讯作者Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要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.
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]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000742179600002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/47048]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Xu, Changsheng
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China
3.Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300000, 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
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.

入库方式： OAI收割

来源：自动化研究所

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