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A method for video processing via an artificial neural network includes receiving a video stream as an input at the artificial neural network. A residual is computed based on a difference between a first feature of a current frame of the video stream and a second feature of a previous frame ...
A method for video processing via an artificial neural network includes receiving a video stream as an input at the artificial neural network. A residual is computed based on a difference between a first feature of a current frame of the video stream and a second feature of a previous frame ...
Convolution operation in deep convolutional neural networks is the most computationally expensive as compared to other operations. Most of the model computation (FLOPS) in the deep architecture belong to convolution operation. In this paper, we are proposing a novel skip convolution operation that ...
3D CNNs 3D convolution skip-connectionsRNNsFeature concatenationThis paper proposes a novel network architecture for human action recognition. First, we employ a pre-trained spatio-temporal feature extractor to perform spatio-temporal features extraction on videos. Then, several-level spatio-temporal ...
It uses a combination of temporal convolution, recurrent skip parts, and an attention mechanism to make RUL estimation more accurate. The recurrent skip component finds long-term patterns in time series data, while temporal convolution pulls out high-level features from longer ...