Self-supervised Video Representation Learning by Pace Prediction (2008.05861v2)

Abstract: This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction. It stems from the observation that human visual system is sensitive to video pace, e.g., slow motion, a widely used technique in film making. Specifically, given a video played in natural pace, we randomly sample training clips in different paces and ask a neural network to identify the pace for each video clip. The assumption here is that the network can only succeed in such a pace reasoning task when it understands the underlying video content and learns representative spatio-temporal features. In addition, we further introduce contrastive learning to push the model towards discriminating different paces by maximizing the agreement on similar video content. To validate the effectiveness of the proposed method, we conduct extensive experiments on action recognition and video retrieval tasks with several alternative network architectures. Experimental evaluations show that our approach achieves state-of-the-art performance for self-supervised video representation learning across different network architectures and different benchmarks. The code and pre-trained models are available at https://github.com/laura-wang/video-pace.

Self-supervised Video Representation Learning by Pace Prediction

The paper "Self-supervised Video Representation Learning by Pace Prediction" by Jiangliu Wang et al. introduces a novel approach for video representation learning within the paradigm of self-supervised learning. The primary focus of the paper is to leverage video pace prediction as an unsupervised task to facilitate the learning of spatio-temporal features that are transferable to various video-related tasks such as action recognition and video retrieval.

Core Contribution

The authors propose a self-supervised learning method wherein a neural network is trained to identify the pace at which video clips are played. This method is based on the premise that the ability to distinguish different paces necessitates understanding the intrinsic video content, and thus encourages the network to learn meaningful representations. This technique circumvents the need for pre-computed motion channels—such as optical flow—thereby simplifying the pretext task and significantly improving efficiency when working with large-scale datasets.

Methodology

The proposed methodology involves:

1. Pace Prediction Task: Videos are processed by altering their playback speeds to form clips of varying paces. The network is trained to classify these clips according to their respective paces. The assumption is that successful pace differentiation requires comprehensive semantic understanding of the video content.
2. Contrastive Learning: Complementary to the primary task, the method employs contrastive learning mechanisms to further enhance feature discrimination. Two strategies are explored:
   • Same Context: Encourages model robustness by maximizing the agreement between different paced clips of the same video.
   • Same Pace: Pulls together feature representations of clips with identical playback paces, although it showed lesser empirical value compared to the same context strategy.

Experimental Results

The effectiveness of the pace prediction task is validated through extensive experiments on action recognition and video retrieval tasks. The key findings are:

• Incorporating pace prediction yields considerable improvements in video representation quality, notably outperforming existing self-supervised methods on standard benchmarks such as UCF101 and HMDB51 when utilizing architectures like R(2+1)D and S3D-G.
• The use of color jittering as a data augmentation technique was found essential in avoiding trivial solutions, thereby forcing the network to learn meaningful semantic features.
• The method achieves state-of-the-art action recognition performance when pre-trained on large-scale datasets like Kinetics-400, demonstrating robust generalization across various network architectures.

Implications and Future Directions

The proposed approach holds significant implications for large-scale video understanding tasks. By minimizing dependency on labeled datasets, it democratizes access to self-supervised learning methods robust enough to leverage the vast quantities of unlabeled video data available today. The integration of contrastive learning hints at pathways for refining multi-objective self-supervised tasks that might offer even richer video representations.

Future developments may involve exploring multi-modality extensions, potentially incorporating audio or textual cues alongside video data to enrich the learned representations further. Additionally, scaling the approach to incorporate more sophisticated architectures and examining its effects in diverse video analysis contexts could provide valuable insights and breakthroughs in self-supervised learning for videos.

In summary, the introduction of pace prediction as a self-supervised task presents a compelling advancement in video representation learning, promising significant impacts on how models for video analysis can be efficiently trained in data-intensive environments.