A method for training a convolutional recurrent neural network for semantic segmentation in videos, includes (a) training, using a set of semantically segmented training images, a first convolutional neural network; (b) training, using a set of semantically segmented training videos, a convolutional recurrent neural network, corresponding to the first convolutional neural network, wherein a convolutional layer has been replaced by a recurrent module having a hidden state. The training of the convolutional recurrent neural network, for each pair of successive frames (t−1, t∈1; T2) of a video of the set of semantically segmented training videos includes warping an internal state of a recurrent layer according to an estimated optical flow between the frames of the pair of successive frames, so as to adapt the internal state to the motion of pixels between the frames of the pair and learning parameters of at least the recurrent module.