SVIP: Sequence VerIfication for Procedures in Videos

doi:10.1109/CVPR52688.2022.01927

	SVIP: Sequence VerIfication for Procedures in Videos
	Qian, Yicheng1 ; Luo, Weixin 2; Lian, Dongze1,5 ; Tang, Xu 3; Zhao, Peilin 4; Gao, Shenghua1,6,7
	2022
会议录名称	PROCEEDINGS OF THE IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION
ISSN	1063-6919
卷号	2022-June
页码	19858-19870
发表状态	已发表
DOI	10.1109/CVPR52688.2022.01927
摘要	In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task. Such a challenging task resides in an open-set setting without prior action detection or segmentation that requires event-level or even frame-level annotations. To that end, we carefully reorganize two publicly available action-related datasets with step-procedure-task structure. To fully investigate the effectiveness of any method, we collect a scripted video dataset enumerating all kinds of step-level transformations in chemical experiments. Besides, a novel evaluation metric Weighted Distance Ratio is introduced to ensure equivalence for different step-level transformations during evaluation. In the end, a simple but effective baseline based on the transformer encoder with a novel sequence alignment loss is introduced to better characterize long-term dependency between steps, which outperforms other action recognition methods. Codes and data will be released1: © 2022 IEEE.
会议名称	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版地	10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
会议地点	New Orleans, LA, United states
会议日期	June 19, 2022 - June 24, 2022
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	National Key R&D Program of China[2018AAA0100704] ; NSFC["61932020","62172279"] ; Science and Technology Commission of Shanghai Municipality[20ZR1436000]
WOS研究方向	Computer Science ; Imaging Science & Photographic Technology
WOS类目	Computer Science, Artificial Intelligence ; Imaging Science & Photographic Technology
WOS记录号	WOS:000870783005068
出版者	IEEE Computer Society
EI入藏号	20224613119907
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251432
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_高盛华组信息科学与技术学院_博士生
通讯作者	Gao, Shenghua
作者单位	1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Meituan, Beijing, Peoples R China 3.Xiaohongshu Inc, Shanghai, Peoples R China 4.Tencent AI Lab, Shenzhen, Peoples R China 5.Natl Univ Singapore, Singapore, Singapore 6.Engn Res Ctr Intelligent Vis & Imaging, Shanghai, Peoples R China 7.Shanghai Engn Res Ctr Energy Efficient & Custom A, Shanghai, Peoples R China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Qian, Yicheng,Luo, Weixin,Lian, Dongze,et al. SVIP: Sequence VerIfication for Procedures in Videos[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE Computer Society,2022:19858-19870.