  A rich representation of the information in video data can be realized by
means of frequency analysis. Fine motion details from the boundaries of moving
regions are characterized by high frequencies in the spatio-temporal domain.
Meanwhile, lower frequencies are encoded with coarse information containing
substantial redundancy, which causes low efficiency for those video models that
take as input raw RGB frames. In this work, we propose a Motion Band-pass
Module (MBPM) for separating the fine-grained information from coarse
information in raw video data. By representing the coarse information with low
resolution, we can increase the efficiency of video data processing. By
embedding the MBPM into a two-pathway CNN architecture, we define a FineCoarse
network. The efficiency of the FineCoarse network is determined by avoiding the
redundancy in the feature space processed by the two pathways: one operates on
downsampled features of low-resolution data, while the other operates on the
fine-grained motion information captured by the MBPM. The proposed FineCoarse
network outperforms many recent video processing models on Kinetics400, UCF101
and HMDB51. Furthermore, our approach achieves the state-of-the-art with 57.0%
top-1 accuracy on Something-Something V1.
