We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy. In such a case, different modalities of the same video are treated as positives and video clips from a different video are treated as negatives. Because the spatio-temporal information is important for video representation, we extend the negative samples by introducing intra-negative samples, which are transformed from the same anchor video by breaking temporal relations in video clips. With the proposed inter-intra contrastive framework, we can train spatio-temporal convolutional networks to learn video representations. There are many flexible options in our proposed framework and we conduct experiments by using several different configurations. Evaluations are conducted on video retrieval and video recognition tasks using the learned video representation. Our proposed methods outperform current state-of-the-art results by a large margin, such as 16.7% and 9.5% points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively. For video recognition, improvements can also be obtained on these two benchmark datasets.

本文提出了一种基于自我监督学习的视频特征表示方法，在传统自我监督学习的基础上通过引入内部负样本来扩展负样本集，从而使单个视频更加充分地表达时空信息，并能够有效地提高视频检索和识别的性能。

利用Inter-intra对比框架进行自监督视频表示学习