SlowFast Networks for Video Recognition

被引：2347

作者：

Feichtenhofer, Christoph ^{[1
]}

Fan, Haoqi ^{[1
]}

Malik, Jitendra ^{[1
]}

He, Kaiming ^{[1
]}

机构：

[1] Facebook AI Res FAIR, Menlo Pk, CA 94025 USA

来源：

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) | 2019年

关键词：

FORM;

D O I：

10.1109/ICCV.2019.00630

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：6201 / 6210

页数：10

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