Disentangling Video with Independent Prediction

This addresses the challenge of interpretable video analysis for computer vision researchers, but appears incremental as it builds on existing variational models.

The paper tackles the problem of unsupervised disentanglement of video into independent factors, where each factor's future can be predicted from its past without considering others, and shows that the approach often learns interpretable factors as objects in a scene.