Object detection in 20 questions

Xi Stephen Chen, He He, Larry S. Davis

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

Abstract

We propose a novel general strategy for object detection. Instead of passively evaluating all object detectors at all possible locations in an image, we develop a divide-and-conquer approach by actively and sequentially evaluating contextual cues related to the query based on the scene and previous evaluations - like playing a "20 Questions" game - to decide where to search for the object. We formulate the problem as a Markov Decision Process and learn a search policy by reinforcement learning. To demonstrate the efficacy of our generic algorithm, we apply the 20 questions approach in the recent framework of simultaneous object detection and segmentation. Experimental results on the Pascal VOC dataset show that our algorithm reduces about 45.3% of the object proposals and 36% of average evaluation time while achieving better average precision compared to exhaustive search.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period3/7/163/10/16

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ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Chen, X. S., He, H., & Davis, L. S. (2016). Object detection in 20 questions. In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 [7477562] (2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2016.7477562