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- W3014138533 abstract "True understanding of videos comes from a joint analysis of all its modalities: the video frames, the audio track, and any accompanying text such as closed captions. We present a way to learn a compact multimodal feature representation that encodes all these modalities. Our model parameters are learned through a proxy task of inferring the temporal ordering of a set of unordered videos in a timeline. To this end, we create a new multimodal dataset for temporal ordering that consists of approximately 30K scenes (2-6 clips per scene) based on the Large Scale Movie Description Challenge. We analyze and evaluate the individual and joint modalities on three challenging tasks: (i) inferring the temporal ordering of a set of videos; and (ii) action recognition. We demonstrate empirically that multimodal representations are indeed complementary, and can play a key role in improving the performance of many applications." @default.
- W3014138533 created "2020-04-10" @default.
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- W3014138533 date "2020-04-05" @default.
- W3014138533 modified "2023-09-27" @default.
- W3014138533 title "Deep Multimodal Feature Encoding for Video Ordering" @default.
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