Unsupervised learning for maximum consensus robust fitting: A reinforcement learning approach

Document Type

Journal Article

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Publisher

IEEE

School

School of Science

RAS ID

44438

Funders

Australian Research Council

Grant Number

ARC Number : DP200103448

Comments

G. Truong, H. Le, E. Zhang, D. Suter and S. Z. Gilani, (2023). Unsupervised learning for maximum consensus robust fitting: A reinforcement learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3890-3903.

https://doi.org/10.1109/TPAMI.2022.3178442

Abstract

Robust model fitting is a core algorithm in several computer vision applications. Despite being studied for decades, solving this problem efficiently for datasets that are heavily contaminated by outliers is still challenging: due to the underlying computational complexity. A recent focus has been on learning-based algorithms. However, most of these 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 (without labelled data) solve robust model fitting. Moreover, unlike other learning-based 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 outperforms existing (un)supervised learning approaches, and also achieves competitive results compared to traditional (non-learning-based) methods. Our approach is designed to try to maximise consensus (MaxCon), similar to the popular RANSAC. The basis of our approach, is to adopt a Reinforcement Learning framework. This requires designing appropriate reward functions, and state encodings. We provide a family of reward functions, tunable by choice of a parameter. We also investigate the application of different basic and enhanced Q-learning components.

DOI

10.1109/TPAMI.2022.3178442

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Link to publisher version (DOI)

10.1109/TPAMI.2022.3178442