Health, Americas, Special Report

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ktvptmmy

Health, Americas, Special Report

Post by ktvptmmy »

2020) directly predict the temporal boundary of the video segment. However, these methods are data-hungry, requiring a large amount of fully annotated data. For instance, the widely used ActivityNet Captions dataset contains 20,000 videos and 100,000 matched sentence queries with their corresponding segment boundaries. Manually annotating such a huge amount of data is very time-consuming and labor-intensive. To alleviate this problem, recent works explore a weakly-supervised setting (Bojanowski et al. 2015; Mithun, Paul, and Roy-Chowdhury 2019; Song et al. 2020; Zhang et al. Though they leave out the segment annotations in training, these methods still need to access the abundant knowledge of the matched video-query pairs. In this paper, we focus on how to learn a video grounding model without any supervision, which excludes both paired video-query knowledge and corresponding segment annotations, as shown in Figure 1 (b). Considering there is no annotated information, what we can access to is only the internal information in the queries and videos.




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