METHOD FOR TRAINING CONVOLUTIONAL RECURRENT NEURAL NETWORK, AND INPUTTED VIDEO SEMANTIC SEGMENTATION METHOD USING TRAINED CONVOLUTIONAL RECURRENT NEURAL NETWORK

摘要

PROBLEM TO BE SOLVED: To provide a method for training a convolutional recurrent neural network for the semantic segmentation of a video.;SOLUTION: The method includes the steps of: training a first convolutional neural network using a set of semantically segmented training images; and training a convolutional recurrent neural network that corresponds to the first convolutional neural network using a set of semantically segmented training videos. The convolution layer is substituted for by a recurrent model having a hidden state. The step for training the recurrent neural network includes a step for warping the internal state of a recurrent layer by an optical flow estimated for the contiguous frame pairs t-1, t of the training video set so that the internal state adapts to pixel motion between paired frames, and a step for learning at least the recurrent module.;SELECTED DRAWING: Figure 4;COPYRIGHT: (C)2020,JPO&INPIT