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We present V<sc>ideo</sc>W<sc>hisper</sc>, a novel approach for unsupervised video representation learning. Based on the observation that the frame sequence encodes the temporal dynamics of a video (e.g., object movement and event evolution), we treat the frame sequential order as a self-supervision to learn video representations. Unlike other unsupervised video feature learning...
As there is a large gap between high-level semantics and low-level features, it is difficult to obtain high-accuracy video semantic annotation through automatic methods. In this paper, we propose a novel automatic video annotation method, which greatly improves the annotation performance by learning from unlabeled video data, as well as exploring temporal consistency of video sequences. To effectively...
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