Research

We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets; 2) A homogeneous architecture with small 3x3x3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets; and 3) Our learned features, namely C3D (Convolutional 3D), significantly outperform state-of-the-art methods on 4 different video analysis tasks and 6 different benchmarks with a simple linear SVM. In addition, the features are compact: achieving 52.8% accuracy on UCF101 dataset with only 10 dimensions and also very efficient to compute: 91 times faster than the current best hand-crafted features and approximately 2 orders of magnitude faster than deep learning based video classification method using optical flow. Finally, they are conceptually very simple and easy to train and use.