  Typical video classification methods often divide a video into short clips,
do inference on each clip independently, then aggregate the clip-level
predictions to generate the video-level results. However, processing visually
similar clips independently ignores the temporal structure of the video
sequence, and increases the computational cost at inference time. In this
paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for
Spatio-TEmporal Redundancy. FASTER aims to leverage the redundancy between
neighboring clips and reduce the computational cost by learning to aggregate
the predictions from models of different complexities. The FASTER framework can
integrate high quality representations from expensive models to capture subtle
motion information and lightweight representations from cheap models to cover
scene changes in the video. A new recurrent network (i.e., FAST-GRU) is
designed to aggregate the mixture of different representations. Compared with
existing approaches, FASTER can reduce the FLOPs by over 10x? while maintaining
the state-of-the-art accuracy across popular datasets, such as Kinetics,
UCF-101 and HMDB-51.
