Knowledge Commons of Institute of Automation,CAS
Weakly-Supervised Video Object Grounding Via Learning Uni-Modal Associations | |
Wang, Wei1,2; Gao, Junyu1,2; Xu, Changsheng1,2,3 | |
发表期刊 | IEEE Transactions on Multimedia |
ISSN | 1520-9210 |
2022 | |
卷号 | 25页码:1-12 |
通讯作者 | Xu, Changsheng(csxu@nlpr.ia.ac.cn) |
摘要 | Grounding objects described in natural language to visual regions in the video is a crucial capability needed in vision-and-language fields. In this paper, we deal with the weakly-supervised video object grounding (WSVOG) task, where only video-sentence pairs are provided for learning. The essence of this task is to learn the cross-modal associations between words in textual modality and regions in visual modality. Despite the recent progress, we find that most existing methods focus on the association learning for cross-modal samples, while the rich and complementary information within uni-modal samples has not been fully exploited. To this end, we propose to explicitly learn uni-modal associations on both textual and visual sides, so as to fully exploit the useful uni-modal information for accurate video object grounding. Specifically, (1) we learn textual prototypes by considering rich contextual information of the same object in different sentences, and (2) we estimate visual prototypes in an adaptive manner so as to overcome the uncertainties in selecting object-relevant visual regions. Besides, a cross-modal correspondence is learned which not only bridges the visual and textual modalities for WSVOG task, but also tightly cooperates with the uni-modal association learning process. We conduct extensive experiments on three popular datasets, and the favorable results demonstrate the effectiveness of our method. |
关键词 | Visualization Grounding Task analysis Prototypes Annotations Uncertainty Proposals Cross-modal retrieval weakly-supervised learning video object grounding uni-modal association |
DOI | 10.1109/TMM.2022.3207581 |
关键词[WOS] | LANGUAGE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research & Development Plan of China[2020AAA0106200] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[62102415] ; National Natural Science Foundation of China[U21B2044] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[62072286] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[62072455] ; National Natural Science Foundation of China[62002355] ; Beijing Natural Science Foundation[L201001] ; Open Research Projects of Zhejiang Lab[2022RC0AB02] ; CCF-Hikvision Open Fund[20210004] |
项目资助者 | National Key Research & Development Plan of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Open Research Projects of Zhejiang Lab ; CCF-Hikvision Open Fund |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:001098831500048 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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/173211/51522 |
专题 | 多模态人工智能系统全国重点实验室 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.PengCheng Laboratory |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Wei,Gao, Junyu,Xu, Changsheng. Weakly-Supervised Video Object Grounding Via Learning Uni-Modal Associations[J]. IEEE Transactions on Multimedia,2022,25:1-12. |
APA | Wang, Wei,Gao, Junyu,&Xu, Changsheng.(2022).Weakly-Supervised Video Object Grounding Via Learning Uni-Modal Associations.IEEE Transactions on Multimedia,25,1-12. |
MLA | Wang, Wei,et al."Weakly-Supervised Video Object Grounding Via Learning Uni-Modal Associations".IEEE Transactions on Multimedia 25(2022):1-12. |
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