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Enhancing Video Summarization via Vision-Language Embedding
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
This paper addresses video summarization, or the problem of distilling a raw video into a shorter form while still capturing the original story. We show that visual representations supervised by freeform language make a good fit for this application by extending a recent submodular summarization approach [9] with representativeness and interestingness objectives computed on features from a joint vision-language embedding space. We perform an evaluation on two diverse datasets, UT Egocentric
doi:10.1109/cvpr.2017.118
dblp:conf/cvpr/PlummerBL17
fatcat:m3pmjulzaradhknim4kdgh2bk4