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.

Learning Spatiotemporal Features with 3D Convolutional Networks Du Tran1,2, Lubomir Bourdev1, Rob Fergus1, Lorenzo Torresani2, Manohar Paluri1 1Facebook AI Research, 2Dartmouth College {dutran,lorenzo}@cs.dartmouth.edu {lubomir,robfergus,mano}@fb.com Abstract

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