When it comes to neural networks learning motion, it’s all relative

Seeking to explore the capabilities of neural networks for recognizing and predicting motion, a group of researchers led by Hehe Fan developed and tested a deep learning approach based on relative change in position encoded as a series of vectors, finding that their method worked better than existing frameworks for modeling motion. The group's key innovation was to encode motion separately from position.