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- W2949888942 abstract "The need for efficiently finding the video content a user wants is increasing because of the erupting of user-generated videos on the Web. Existing keyword-based or content-based video retrieval methods usually determine what occurs in a video but not when and where. In this paper, we make an answer to the question of when and where by formulating a new task, namely spatio-temporal video re-localization. Specifically, given a query video and a reference video, spatio-temporal video re-localization aims to localize tubelets in the reference video such that the tubelets semantically correspond to the query. To accurately localize the desired tubelets in the reference video, we propose a novel warp LSTM network, which propagates the spatio-temporal information for a long period and thereby captures the corresponding long-term dependencies. Another issue for spatio-temporal video re-localization is the lack of properly labeled video datasets. Therefore, we reorganize the videos in the AVA dataset to form a new dataset for spatio-temporal video re-localization research. Extensive experimental results show that the proposed model achieves superior performances over the designed baselines on the spatio-temporal video re-localization task." @default.
- W2949888942 created "2019-06-27" @default.
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- W2949888942 date "2019-06-01" @default.
- W2949888942 modified "2023-09-29" @default.
- W2949888942 title "Spatio-Temporal Video Re-Localization by Warp LSTM" @default.
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- W2949888942 doi "https://doi.org/10.1109/cvpr.2019.00138" @default.
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