SpeedNet: Learning the Speediness in Videos
| Autor: | Tali Dekel, Sagie Benaim, Ariel Ephrat, Michal Irani, Inbar Mosseri, Oran Lang, William T. Freeman, Michael Rubinstein |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Contextual image classification Artificial neural network Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Visualization Range (mathematics) 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Computer vision Artificial intelligence Representation (mathematics) business Sensory cue |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.2004.06130 |
| Popis: | We wish to automatically predict the "speediness" of moving objects in videos---whether they move faster, at, or slower than their "natural" speed. 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. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. 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. 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 how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval. 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. Comment: Accepted to CVPR 2020 (oral). Project webpage: http://speednet-cvpr20.github.io |
| Databáze: | OpenAIRE |
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