  We present SlowFast networks for video recognition. Our model involves (i) a
Slow pathway, operating at low frame rate, to capture spatial semantics, and
(ii) a Fast pathway, operating at high frame rate, to capture motion at fine
temporal resolution. The Fast pathway can be made very lightweight by reducing
its channel capacity, yet can learn useful temporal information for video
recognition. Our models achieve strong performance for both action
classification and detection in video, and large improvements are pin-pointed
as contributions by our SlowFast concept. We report state-of-the-art accuracy
on major video recognition benchmarks, Kinetics, Charades and AVA. Code has
been made available at: https://github.com/facebookresearch/SlowFast
