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The work in this paper is driven by the question how to exploit the temporalcues available in videos for their accurate classification, and for humanaction recognition in particular? Thus far, the vision community has focused onspatio-temporal approaches with fixed temporal convolution kernel depths. Weintroduce a new temporal layer that models variable temporal convolution kerneldepths. We embed this new temporal layer in our proposed 3D CNN. We extend theDenseNet architecture - which normally is 2D - with 3D filters and poolingkernels. We name our proposed video convolutional network `Temporal 3DConvNet'~(T3D) and its new temporal layer `Temporal Transition Layer'~(TTL).Our experiments show that T3D outperforms the current state-of-the-art methodson the HMDB51, UCF101 and Kinetics datasets.  The other issue in training 3D ConvNets is about training them from scratchwith a huge labeled dataset to get a reasonable performance. So the knowledgelearned in 2D ConvNets is completely ignored. Another contribution in this workis a simple and effective technique to transfer knowledge from a pre-trained 2DCNN to a randomly initialized 3D CNN for a stable weight initialization. Thisallows us to significantly reduce the number of training samples for 3D CNNs.Thus, by finetuning this network, we beat the performance of generic and recentmethods in 3D CNNs, which were trained on large video datasets, e.g. Sports-1M,and finetuned on the target datasets, e.g. HMDB51/UCF101. The T3D codes will bereleased

Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification