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Learning image representations with ConvNets by pre-training on ImageNet hasproven useful across many visual understanding tasks including objectdetection, semantic segmentation, and image captioning. Although any imagerepresentation can be applied to video frames, a dedicated spatiotemporalrepresentation is still vital in order to incorporate motion patterns thatcannot be captured by appearance based models alone. This paper presents anempirical ConvNet architecture search for spatiotemporal feature learning,culminating in a deep 3-dimensional (3D) Residual ConvNet. Our proposedarchitecture outperforms C3D by a good margin on Sports-1M, UCF101, HMDB51,THUMOS14, and ASLAN while being 2 times faster at inference time, 2 timessmaller in model size, and having a more compact representation.

ConvNet Architecture Search for Spatiotemporal Feature Learning