  Existing action recognition methods typically sample a few frames to
represent each video to avoid the enormous computation, which often limits the
recognition performance. To tackle this problem, we propose Ample and Focal
Network (AFNet), which is composed of two branches to utilize more frames but
with less computation. Specifically, the Ample Branch takes all input frames to
obtain abundant information with condensed computation and provides the
guidance for Focal Branch by the proposed Navigation Module; the Focal Branch
squeezes the temporal size to only focus on the salient frames at each
convolution block; in the end, the results of two branches are adaptively fused
to prevent the loss of information. With this design, we can introduce more
frames to the network but cost less computation. Besides, we demonstrate AFNet
can utilize fewer frames while achieving higher accuracy as the dynamic
selection in intermediate features enforces implicit temporal modeling.
Further, we show that our method can be extended to reduce spatial redundancy
with even less cost. Extensive experiments on five datasets demonstrate the
effectiveness and efficiency of our method.
