CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization

Yuxi Li, Weiyao Lin*, John See, Ning Xu, Shugong Xu, Ke Yan, Cong Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD), an original end-to-end trainable framework for efficient spatio-temporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatio-temporal action tubes from video streams, and then refines the tubes’ location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our framework. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, the proposed CFAD achieves competitive results on action detection benchmarks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3 faster than the nearest competitor.

Original languageEnglish
Title of host publicationComputer Vision. ECCV 2020
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
Number of pages18
ISBN (Electronic)9783030585174
ISBN (Print)9783030585167
Publication statusPublished - 10 Oct 2020
Event16th European Conference on Computer Vision 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Computer Vision 2020
Abbreviated titleECCV 2020
Country/TerritoryUnited Kingdom


  • Coarse-to-fine paradigm
  • Parameterized modeling
  • Spatiotemporal action detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization'. Together they form a unique fingerprint.

Cite this