"D3D: Dual 3-D Convolutional Network for Real-Time Action Recognition" by Shengqin Jiang, Yuankai Qi et al.
Abstract Three-dimensional convolutional neural networks (3D CNNs) have been explored to learn spatio-temporal information for video-based human action recognition. Expensive computational cost and memory demand resulted from standard 3D CNNs, however, hinder their application in practical scenarios. In this article, we address the aforementioned limitations by proposing a novel dual 3-D convolutional network (D3DNet) with two complementary lightweight branches. A coarse branch maintains large temporal receptive field by a fast temporal downsampling strategy and simulates the expensive 3-D co...
Source: uow.edu.au