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Learning Hierarchical Video Graph Networks for One-Stop Video Delivery.
- Source :
- ACM Transactions on Multimedia Computing, Communications & Applications; Jan2022, Vol. 18 Issue 1, p1-23, 23p
- Publication Year :
- 2022
-
Abstract
- The explosive growth of video data has brought great challenges to video retrieval, which aims to find out related videos from a video collection. Most users are usually not interested in all the content of retrieved videos but have a more fine-grained need. In the meantime, most existing methods can only return a ranked list of retrieved videos lacking a proper way to present the video content. In this paper, we introduce a distinctively new task, namely One-Stop Video Delivery (OSVD) aiming to realize a comprehensive retrieval system with the following merits: it not only retrieves the relevant videos but also filters out irrelevant information and presents compact video content to users, given a natural language query and video collection. To solve this task, we propose an end-to-end Hierarchical Video Graph Reasoning framework (HVGR), which considers relations of different video levels and jointly accomplishes the one-stop delivery task. Specifically, we decompose the video into three levels, namely the video-level, moment-level, and the clip-level in a coarse-to-fine manner, and apply Graph Neural Networks (GNNs) on the hierarchical graph to model the relations. Furthermore, a pairwise ranking loss named Progressively Refined Loss is proposed based on prior knowledge that there is a relative order of the similarity of query-video, query-moment, and query-clip due to the different granularity of matched information. Extensive experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance compared with baseline methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- VIDEOS
NATURAL languages
Subjects
Details
- Language :
- English
- ISSN :
- 15516857
- Volume :
- 18
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- ACM Transactions on Multimedia Computing, Communications & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 155927517
- Full Text :
- https://doi.org/10.1145/3466886