Learning to capture both long-range and short-range temporal information is crucial for action recognition task. Previous works utilize 3D ConvNets to capture short-range temporal dynamics in replacement of optical-flow which needs time-consuming extraction. However, dramatically incresed parameters limit the capacity for modeling long-term interactions. In this paper, we propose Segments-based 3D ConvNet (S3D) to integrate both long-term and short-term temporal dynamics. Firstly, we utilize 3D ResNet without temporal downsampling to capture short-range video contents. Secondly, we integrate a sparse sampling strategy to model long-range temporal structure. Finally, experiments on UCF-101 and HMDB-51 datasets show the effectiveness of our S3D compared with corresponding 3D ConvNet.

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