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We propose a simple, yet effective approach for spatiotemporal featurelearning using deep 3-dimensional convolutional networks (3D ConvNets) trainedon a large scale supervised video dataset. Our findings are three-fold: 1) 3DConvNets are more suitable for spatiotemporal feature learning compared to 2DConvNets; 2) A homogeneous architecture with small 3x3x3 convolution kernels inall layers is among the best performing architectures for 3D ConvNets; and 3)Our learned features, namely C3D (Convolutional 3D), with a simple linearclassifier outperform state-of-the-art methods on 4 different benchmarks andare comparable with current best methods on the other 2 benchmarks. Inaddition, the features are compact: achieving 52.8% accuracy on UCF101 datasetwith only 10 dimensions and also very efficient to compute due to the fastinference of ConvNets. Finally, they are conceptually very simple and easy totrain and use.

Learning Spatiotemporal Features with 3D Convolutional Networks