  This paper provides a review on representation learning for videos. We
classify recent spatiotemporal feature learning methods for sequential visual
data and compare their pros and cons for general video analysis. Building
effective features for videos is a fundamental problem in computer vision tasks
involving video analysis and understanding. Existing features can be generally
categorized into spatial and temporal features. Their effectiveness under
variations of illumination, occlusion, view and background are discussed.
Finally, we discuss the remaining challenges in existing deep video
representation learning studies.
