Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction

Understand

The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections.

• To alleviate this problem, in this paper, we propose 3DRotNet: a fully self-supervised approach to learn spatiotemporal features from unlabeled videos.
• A set of rotations are applied to all videos, and a pretext task is defined as prediction of these rotations.
• When accomplishing this task, 3DRotNet is actually trained to understand the semantic concepts and motions in videos.