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https://hdl.handle.net/2440/139823
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Type: | Conference paper |
Title: | Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach |
Author: | Truong, G. Le, H. Suter, D. Zhang, E. Gilani, S.Z. |
Citation: | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021, pp.10343-10352 |
Publisher: | IEEE |
Issue Date: | 2021 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781665445092 |
ISSN: | 1063-6919 2575-7075 |
Conference Name: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (19 Jun 2021 - 25 Jun 2021 : virtual online) |
Statement of Responsibility: | Giang Truong, Huu Le, David Suter, Erchuan Zhang, Syed Zulqarnain Gilani |
Abstract: | Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, however, still challenging due to the underlying computational complexity. Recent literature has focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasiconvex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems¹. |
Rights: | ©2021 IEEE |
DOI: | 10.1109/CVPR46437.2021.01021 |
Grant ID: | http://purl.org/au-research/grants/arc/DP200103448 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/9577055/proceeding |
Appears in Collections: | Computer Science publications |
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