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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2946823
|2 doi
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|a pubmed24n1007.xml
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|a (DE-627)NLM302202749
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Yan, Yichao
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Fine-Grained Video Captioning via Graph-based Multi-Granularity Interaction Learning
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| 264 |
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 28.03.2022
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|a Date Revised 01.04.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a Learning to generate continuous linguistic descriptions for multi-subject interactive videos in great details has particular applications in team sports auto-narrative. In contrast to traditional video caption, this task is more challenging as it requires simultaneous modeling of fine-grained individual actions, uncovering of spatio-temporal dependency structures of frequent group interactions, and then accurate mapping of these complex interaction details into long and detailed commentary. To explicitly address these challenges, we propose a novel framework Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR) for fine-grained team sports auto-narrative task. A multi-granular interaction modeling module is proposed to extract among-subjects' interactive actions in a progressive way for encoding both intra- and inter-team interactions. Based on the above multi-granular representations, a multi-granular attention module is developed to consider action/event descriptions of multiple spatio-temporal resolutions. Both modules are integrated seamlessly and work in a collaborative way to generate the final narrative. In the meantime, to facilitate reproducible research, we collect a new video dataset from YouTube.com called Sports Video Narrative dataset (SVN). It is a novel direction as it contains 6K team sports videos (i.e., NBA basketball games) with 10K ground-truth narratives(e.g., sentences). Furthermore, as previous metrics such as METEOR (i.e., used in coarse-grained video caption task) DO NOT cope with fine-grained sports narrative task well, we hence develop a novel evaluation metric named Fine-grained Captioning Evaluation (FCE), which measures how accurate the generated linguistic description reflects fine-grained action details as well as the overall spatio-temporal interactional structure. Extensive experiments on our SVN dataset have demonstrated the effectiveness of the proposed framework for fine-grained team sports video auto-narrative
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zhuang, Ning
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ni, Bingbing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Jian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Minghao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Qiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Zheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheng, Shuo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tian, Qi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Yi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Xiaokang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Wenjun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 2 vom: 15. Feb., Seite 666-683
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:44
|g year:2022
|g number:2
|g day:15
|g month:02
|g pages:666-683
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2019.2946823
|3 Volltext
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