Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features
Dubravko R. Culibrk, Nicu Sebe · 2014
The paper addresses the problem of learning features that can account for temporal dynamics present in videos. Although deep convolutional learning methods revolutionized several areas of multimedia and computer vision, there have been relatively few proposals dealing with ways in which these methods can be enabled to make use of motion information, critical to the extraction of useful information from video. We propose a temporal dropout of changes approach for this, which allows us to consider temporal information over a series of frames without increasing the number of training parameters of the network.