Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

Abstract

In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal… (More)
DOI: 10.1007/978-3-319-46448-0_32

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