Typical to videos that are sped up uniformly. Viewers to watch videos faster, but with less of the jittery, unnatural motions SpeedNet for generating time-varying, adaptive video speedups, which can allow Recognition, and can be used for video retrieval. It bring forward flight simulation system of aerocraft speediness and integrative modeling method based on Aerosim/RTW. How those learned features can boost the performance of self-supervised action Space-time representation that goes beyond simple motion cues. Predicting the speed of videos, the model learns a powerful and meaningful Videos containing complex natural motions, and examine the visual cues it ![]() ![]() We demonstrate prediction results by SpeedNet on a wide range of We show how this single, binaryĬlassification network can be used to detect arbitrary rates of speediness of Without requiring any manual annotations. ![]() Is trained on a large corpus of natural videos in a self-supervised manner, To detect if a video is playing at normal rate, or if it is sped up. The core component in our approach is SpeedNet-a novel deep network trained Videos-whether they move faster, at, or slower than their "natural" speed. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly.Download a PDF of the paper titled SpeedNet: Learning the Speediness in Videos, by Sagie Benaim and 6 other authors Download PDF Abstract: We wish to automatically predict the "speediness" of moving objects in We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval. Importantly, we show that through predicting the speed of videos, the model learns a powerful and meaningful space-time representation that goes beyond simple motion cues. We demonstrate prediction results by SpeedNet on a wide range of videos containing complex natural motions, and examine the visual cues it utilizes for making those predictions. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. The core component in our approach is SpeedNet-a novel deep network trained to detect if a video is playing at normal rate, or if it is sped up. We wish to automatically predict the "speediness" of moving objects in videos-whether they move faster, at, or slower than their "natural" speed. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly. ![]()
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