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."/>
METHOD FOR TRAINING A CONVOLUTIONAL RECURRENT NEURAL NETWORK AND FOR SEMANTIC SEGMENTATION OF INPUTTED VIDEO USING THE TRAINED CONVOLUTIONAL RECURRENT NEURAL NETWORK
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METHOD FOR TRAINING A CONVOLUTIONAL RECURRENT NEURAL NETWORK AND FOR SEMANTIC SEGMENTATION OF INPUTTED VIDEO USING THE TRAINED CONVOLUTIONAL RECURRENT NEURAL NETWORK
METHOD FOR TRAINING A CONVOLUTIONAL RECURRENT NEURAL NETWORK AND FOR SEMANTIC SEGMENTATION OF INPUTTED VIDEO USING THE TRAINED CONVOLUTIONAL RECURRENT NEURAL NETWORK
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recurrent neural network, corresponding to the first convolutional neural network, wherein a convolutional layer has been replaced by a recurrent module having a hidden state.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.
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机译:一种用于训练卷积递归神经网络进行视频中语义分割的方法,包括:(a)使用一组语义分割的训练图像训练第一卷积神经网络;(b)使用一组语义分割的训练视频进行训练,对应于第一卷积神经网络的卷积递归神经网络,其中卷积层已被具有隐藏状态的递归模块替换。对每对连续帧(t-1,t∈ 1; T 2 Sup>)包括根据视频流之间的估计光流扭曲循环层的内部状态一对连续帧中的两个帧,以使内部状态适应该对帧之间的像素运动以及至少循环模块的学习参数。
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