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Reducing Vision-Answer Biases for Multiple-Choice VQA

文献类型：期刊论文

作者Zhang, Xi1,5; Zhang, Feifei2,3; Xu, Changsheng1,4,5
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:4621-4634
关键词Multiple-choice VQA vision-answer bias causal intervention counterfactual interaction learning
ISSN号1057-7149
DOI10.1109/TIP.2023.3302162
通讯作者Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要Multiple-choice visual question answering (VQA) is a challenging task due to the requirement of thorough multimodal understanding and complicated inter-modality relationship reasoning. To solve the challenge, previous approaches usually resort to different multimodal interaction modules. Despite their effectiveness, we find that existing methods may exploit a new discovered bias (vision-answer bias) to make answer prediction, leading to suboptimal VQA performances and poor generalization. To solve the issues, we propose a Causality-based Multimodal Interaction Enhancement (CMIE) method, which is model-agnostic and can be seamlessly incorporated into a wide range of VQA approaches in a plug-and-play manner. Specifically, our CMIE contains two key components: a causal intervention module and a counterfactual interaction learning module. The former devotes to removing the spurious correlation between the visual content and the answer caused by the vision-answer bias, and the latter helps capture discriminative inter-modality relationships by directly supervising multimodal interaction training via an interactive loss. Extensive experimental results on three public benchmarks and one reorganized dataset show that the proposed method can significantly improve seven representative VQA models, demonstrating the effectiveness and generalizability of the CMIE.
WOS关键词QUESTION ; INFERENCE
资助项目National Key Research and Development Plan of China[2021ZD0112200] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[62002355] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[62102415] ; National Natural Science Foundation of China[62072455] ; National Natural Science Foundation of China[62202331] ; National Natural Science Foundation of China[62206200] ; National Natural Science Foundation of China[62106262] ; Tianjin Natural Science Foundation[22JCYBJC00030] ; Beijing Natural Science Foundation[L201001] ; Beijing Natural Science Foundation[4222039]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001049970200005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Tianjin Natural Science Foundation ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/54038]  
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, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Minist Educ, Key Lab Comp Vis & Syst, Tianjin 300384, Peoples R China
3.Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300384, Peoples R China
4.Peng Cheng Lab, Shenzhen 518066, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xi,Zhang, Feifei,Xu, Changsheng. Reducing Vision-Answer Biases for Multiple-Choice VQA[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:4621-4634.
APA Zhang, Xi,Zhang, Feifei,&Xu, Changsheng.(2023).Reducing Vision-Answer Biases for Multiple-Choice VQA.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,4621-4634.
MLA Zhang, Xi,et al."Reducing Vision-Answer Biases for Multiple-Choice VQA".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):4621-4634.

入库方式： OAI收割

来源：自动化研究所

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