Transcription of Learning Spatiotemporal Features With 3D Convolutional ...
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Learning Spatiotemporal Features With 3D Convolutional ...
Learning Spatiotemporal Features with 3D Convolutional NetworksDu Tran1,2, Lubomir Bourdev1, Rob Fergus1, Lorenzo Torresani2, Manohar Paluri11 Facebook AI Research,2 Dartmouth propose a simple, yet effective approach for spa-tiotemporal feature Learning using deep 3-dimensional con-volutional networks (3D ConvNets) trained on a large scalesupervised video dataset. Our findings are three-fold: 1)3D ConvNets are more suitable for Spatiotemporal featurelearning compared to 2D ConvNets; 2) A homogeneous ar-chitecture with small3 3 3convolution kernels in alllayers is among the best performing architectures for 3 DConvNets; and 3) Our learned Features , namely C3D (Con-volutional 3D), with a simple linear classifier outperformstate-of-the-art methods on 4 different benchmarks and arecomparable with current best methods on the other 2 bench-marks. In addition, the Features are compact: on UCF101 dataset with only10dimen-sions and also very efficient to compute due to the fast in-ference of ConvNets.
vary the value dof these layers to search for a good 3D ar-chitecture). All of these convolution layers are applied with appropriate padding (both spatial and temporal) and stride-iDT. 1,,,, ICCV,,, ICCV, Learning Spatiotemporal Features With 3D Convolutional Networks ...
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