Knowledge Commons of Institute of Automation,CAS
Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering | |
Liu, Fei1,2; Liu, Jing1,2; Hong, Richang3; Lu, Hanqing1,2 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2021-08-30 | |
页码 | 0 |
摘要 | Spatiotemporal attention learning for video question answering (VideoQA) has always been a challenging task, where existing approaches treat the attention parts and the nonattention parts in isolation. In this work, we propose to enforce the correlation between the attention parts and the nonattention parts as a distance constraint for discriminative spatiotemporal attention learning. Specifically, we first introduce a novel attention-guided erasing mechanism in the traditional spatiotemporal attention to obtain multiple aggregated attention features and nonattention features and then learn to separate the attention and the nonattention features with an appropriate distance. The distance constraint is enforced by a metric learning loss, without increasing the inference complexity. In this way, the model can learn to produce more discriminative spatiotemporal attention distribution on videos, thus enabling more accurate question answering. In order to incorporate the multiscale spatiotemporal information that is beneficial for video understanding, we additionally develop a pyramid variant on basis of the proposed approach. Comprehensive ablation experiments are conducted to validate the effectiveness of our approach, and state-of-the-art performance is achieved on several widely used datasets for VideoQA. |
关键词 | video question answering attention mechanism metric learning |
DOI | 10.1109/TNNLS.2021.3105280 |
关键词[WOS] | IMAGE SIMILARITY |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020AAA0106400] ; National Natural Science Foundation of China[61922086] ; National Natural Science Foundation of China[61872366] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000733489300001 |
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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47029 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Liu, Jing |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Hefei Univ Technol, Sch Comp & Informat, Hefei 230000, 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 | Liu, Fei,Liu, Jing,Hong, Richang,et al. Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:0. |
APA | Liu, Fei,Liu, Jing,Hong, Richang,&Lu, Hanqing.(2021).Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,0. |
MLA | Liu, Fei,et al."Question-Guided Erasing-Based Spatiotemporal Attention Learning for Video Question Answering".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):0. |
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