Unsupervised learning for robust fitting: A reinforcement learning approach
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 quasi-convex residuals. We empirically show that our method out-performs existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems
RAS ID
36862
Document Type
Conference Proceeding
Date of Publication
2021
Funding Information
Australian Research Council
Wallenberg AI
Autonomous Systems and Software Program Chalmers
AI Research Center (CHAIR) Seed Projects
School
School of Science
Grant Number
ARC Number : DP200103448
Copyright
free_to_read
Publisher
IEEE
Comments
Truong, G., Le, H., Suter, D., Zhang, E., & Gilani, S. Z. (2021). Unsupervised learning for robust fitting: A reinforcement learning approach. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10343-10352). IEEE.
https://doi.org/10.1109/CVPR46437.2021.01021
An open access version of this paper is provided by the Computer Vision Foundation.