SlowFast Networks for Video Recognition

Abstract

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.

Keywords

Computer scienceFrame rateAction recognitionCode (set theory)Artificial intelligenceFrame (networking)Semantics (computer science)Computer visionPattern recognition (psychology)Computer networkProgramming language

Affiliated Institutions

Meta (Israel) IL

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Publication Info

Year: 2019
Type: article
Citations: 3322
Access: Closed

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APA Style

                            
                                    Christoph Feichtenhofer, 
                                
                                    Haoqi Fan, 
                                
                                    Jitendra Malik
                                
                                et al.
                            
                            (2019). 
                            SlowFast Networks for Video Recognition. 
                            
                            .
                            https://doi.org/10.1109/iccv.2019.00630

Identifiers

DOI: 10.1109/iccv.2019.00630