Repeat and learn: Self-supervised visual representations learning by Scene Localization

Large labeled datasets are crucial for video understanding progress. However, the labeling process is timeconsuming, expensive, and tiresome. To overcome this impediment, various pretexts use the temporal coherence in videos to learn visual representations in a self-supervised manner. However, these pretexts (order verification and sequence sorting) struggle when encountering cyclic actions due to the label ambiguity problem. To overcome these limitations, we present a novel temporal pretext task to address self-supervised learning of visual representations from unlabeled videos. Repeated Scene Localization (RSL) is a multi-class classification pretext that involves changing the temporal order of the frames in a video by repeating a scene. Then, the network is trained to identify the modified video, localize the location of the repeated scene, and identify the unmodified original videos that do not have repeated scenes. We evaluated the proposed pretext on two benchmark datasets, UCF-101 and HMDB-51. The experimental results show that the proposed pretext achieves state-of-the-art results in action recognition and video retrieval tasks. In action recognition, our S3D model achieves 88.15% and 56.86% on UCF-101 and HMDB-51, respectively. It outperforms the current state-of-the-art by 1.05% and 3.26%. Our R(2+1)D-Adjacent model achieves 83.52% and 54.50% on UCF-101 and HMDB-51, respectively. It outperforms the single pretext tasks by 8.7% and 13.9%. In video retrieval, our R(2+1)D-Offset model outperforms the single pretext tasks by 4.68% and 1.1% Top 1 accuracies on UCF-101 and HMDB-51, respectively. The source code and the trained models are publicly available at https://github.com/Hussein-A-Hassan/RSL-Pretext.