Deep Reinforcement Learning of Region Proposal Networks for Object Detection
(2018) 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 p.6945-6954- Abstract
We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection process with a sequential attention mechanism which is trained via deep reinforcement learning (RL). Our model is capable of accumulating class-specific evidence over time, potentially affecting subsequent proposals and classification scores, and we show that such context integration significantly boosts detection accuracy. Moreover, drl-RPN... (More)
We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection process with a sequential attention mechanism which is trained via deep reinforcement learning (RL). Our model is capable of accumulating class-specific evidence over time, potentially affecting subsequent proposals and classification scores, and we show that such context integration significantly boosts detection accuracy. Moreover, drl-RPN automatically decides when to stop the search process and has the benefit of being able to jointly learn the parameters of the policy and the detector, both represented as deep networks. Our model can further learn to search over a wide range of exploration-accuracy trade-offs making it possible to specify or adapt the exploration extent at test time. The resulting search trajectories are image- and category-dependent, yet rely only on a single policy over all object categories. Results on the MS COCO and PASCAL VOC challenges show that our approach outperforms established, typical state-of-the-art object detection pipelines.
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- author
- Pirinen, Aleksis LU and Sminchisescu, Cristian LU
- organization
- publishing date
- 2018-12-17
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
- article number
- 8578824
- pages
- 10 pages
- publisher
- IEEE Computer Society
- conference name
- 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
- conference location
- Salt Lake City, United States
- conference dates
- 2018-06-18 - 2018-06-22
- external identifiers
-
- scopus:85062828217
- ISBN
- 9781538664209
- DOI
- 10.1109/CVPR.2018.00726
- language
- English
- LU publication?
- yes
- id
- 5610ef4e-3535-43a3-b501-5fda2996c757
- date added to LUP
- 2019-04-01 09:52:21
- date last changed
- 2022-05-03 18:57:08
@inproceedings{5610ef4e-3535-43a3-b501-5fda2996c757, abstract = {{<p>We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection process with a sequential attention mechanism which is trained via deep reinforcement learning (RL). Our model is capable of accumulating class-specific evidence over time, potentially affecting subsequent proposals and classification scores, and we show that such context integration significantly boosts detection accuracy. Moreover, drl-RPN automatically decides when to stop the search process and has the benefit of being able to jointly learn the parameters of the policy and the detector, both represented as deep networks. Our model can further learn to search over a wide range of exploration-accuracy trade-offs making it possible to specify or adapt the exploration extent at test time. The resulting search trajectories are image- and category-dependent, yet rely only on a single policy over all object categories. Results on the MS COCO and PASCAL VOC challenges show that our approach outperforms established, typical state-of-the-art object detection pipelines.</p>}}, author = {{Pirinen, Aleksis and Sminchisescu, Cristian}}, booktitle = {{Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018}}, isbn = {{9781538664209}}, language = {{eng}}, month = {{12}}, pages = {{6945--6954}}, publisher = {{IEEE Computer Society}}, title = {{Deep Reinforcement Learning of Region Proposal Networks for Object Detection}}, url = {{http://dx.doi.org/10.1109/CVPR.2018.00726}}, doi = {{10.1109/CVPR.2018.00726}}, year = {{2018}}, }