To alleviate the expensive cost of data collection and annotation, many self-supervised learning methods were proposed to learn image representations without human-labeled annotations. However, self-supervised learning for video representations is not yet well-addressed. In this paper, we propose a novel 3DConvNet-based fully self-supervised framework to learn spatiotemporal video features without using any human-labeled annotations. First, a set of pre-designed geometric transformations (e.g. rotating 0 degree, 90 degrees, 180 degrees, and 270 degrees) are applied to each video. Then a pretext task can be defined as "recognizing the pre-designed geometric transformations." Therefore, the spatiotemporal video features can be learned in the process of accomplishing this pretext task without using human-labeled annotations. The learned spatiotemporal video representations can further be employed as pretrained features for different video-related applications. The proposed geometric transformations (e.g. rotations) are proved to be effective to learn representative spatiotemporal features in our 3DConvNet-based fully self-supervised framework. With the pre-trained spatiotemporal features from two large video datasets, the performance of action recognition is significantly boosted up by 20.4% on UCF101 dataset and 16.7% on HMDB51 dataset respectively compared to that from the model trained from scratch. Furthermore, our framework outperforms the state-of-the-arts of fully self-supervised methods on both UCF101 and HMDB51 datasets and achieves 62.9% and 33.7% accuracy respectively.

本文提出了3DRotNet，一种全自我监管的方法，用于从未标记的视频中学习空间时间特征来提高视频理解任务的性能

通过视频旋转预测进行自监督空时特征学习