Reinforcement learning for visual object detection
(2016) 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 2016-January. p.2894-2902- Abstract
One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration - sampling more image regions for better... (More)
One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration - sampling more image regions for better accuracy-, and exploitation - stopping the search efficiently when sufficiently confident about the target's location. The methodology is general and applicable to any detector response function. We report encouraging results in the PASCAL VOC 2012 object detection test set showing that the proposed methodology achieves almost two orders of magnitude speed-up over sliding window methods.
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- author
- Mathe, Stefan ; Pirinen, Aleksis LU and Sminchisescu, Cristian LU
- organization
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
- volume
- 2016-January
- pages
- 9 pages
- publisher
- IEEE Computer Society
- conference name
- 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
- conference location
- Las Vegas, United States
- conference dates
- 2016-06-26 - 2016-07-01
- external identifiers
-
- scopus:84986295261
- wos:000400012302102
- ISBN
- 9781467388511
- DOI
- 10.1109/CVPR.2016.316
- language
- English
- LU publication?
- yes
- id
- 7a9e0d3d-4779-4f7e-8633-b43597a6338f
- date added to LUP
- 2017-02-08 13:54:44
- date last changed
- 2024-08-04 14:40:33
@inproceedings{7a9e0d3d-4779-4f7e-8633-b43597a6338f, abstract = {{<p>One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration - sampling more image regions for better accuracy-, and exploitation - stopping the search efficiently when sufficiently confident about the target's location. The methodology is general and applicable to any detector response function. We report encouraging results in the PASCAL VOC 2012 object detection test set showing that the proposed methodology achieves almost two orders of magnitude speed-up over sliding window methods.</p>}}, author = {{Mathe, Stefan and Pirinen, Aleksis and Sminchisescu, Cristian}}, booktitle = {{2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016}}, isbn = {{9781467388511}}, language = {{eng}}, pages = {{2894--2902}}, publisher = {{IEEE Computer Society}}, title = {{Reinforcement learning for visual object detection}}, url = {{http://dx.doi.org/10.1109/CVPR.2016.316}}, doi = {{10.1109/CVPR.2016.316}}, volume = {{2016-January}}, year = {{2016}}, }