Knowledge Commons of Institute of Automation,CAS
Reducing Vision-Answer Biases for Multiple-Choice VQA | |
Zhang, Xi1,2; Zhang, Feifei3,4; Xu, Changsheng1,2,5 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2023 | |
卷号 | 32页码:4621-4634 |
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. |
关键词 | Multiple-choice VQA vision-answer bias causal intervention counterfactual interaction learning |
DOI | 10.1109/TIP.2023.3302162 |
关键词[WOS] | QUESTION ; INFERENCE |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Tianjin Natural Science Foundation ; 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:001049970200005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 多模态协同认知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54039 |
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.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Minist Educ, Key Lab Comp Vis & Syst, Tianjin 300384, Peoples R China 4.Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300384, Peoples R China 5.Peng Cheng Lab, Shenzhen 518066, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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