We propose a self-supervised visual learning method by predicting the
variable playback speeds of a video. Without semantic labels, we learn the
spatio-temporal visual representation of the video by leveraging the variations
in the visual appearance according to different playback speeds under the
assumption of temporal coherence. To learn the spatio-temporal visual
variations in the entire video, we have not only predicted a single playback
speed but also generated clips of various playback speeds and directions with
randomized starting points. Hence the visual representation can be successfully
learned from the meta information (playback speeds and directions) of the
video. We also propose a new layer dependable temporal group normalization
method that can be applied to 3D convolutional networks to improve the
representation learning performance where we divide the temporal features into
several groups and normalize each one using the different corresponding
parameters. We validate the effectiveness of our method by fine-tuning it to
the action recognition and video retrieval tasks on UCF-101 and HMDB-51.

提出了一种无需标注数据的自我监督视觉学习方法，通过预测视频播放速度的变化来学习视频的时空视觉表示，同时运用可变播放速度下视觉外观的变化来学习整个视频的时空视觉特征，并提出了一种新的可应用于三维卷积神经网络的可靠的时间组归一化方法，最终在 UCF-101 和 HMDB-51 数据集上验证了该方法的有效性。