no code implementations • CVPR 2022 • Erchuan Zhang, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, Syed Zulqarnain Gilani
In particular, we study endowing the Boolean cube with the Bernoulli measure and performing biased (as opposed to uniform) sampling.
no code implementations • CVPR 2021 • Giang Truong, Huu Le, David Suter, Erchuan Zhang, Syed Zulqarnain Gilani
In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting.
1 code implementation • CVPR 2021 • Ruwan Tennakoon, David Suter, Erchuan Zhang, Tat-Jun Chin, Alireza Bab-Hadiashar
Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level.
1 code implementation • 5 Mar 2021 • Giang Truong, Huu Le, David Suter, Erchuan Zhang, Syed Zulqarnain Gilani
In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting.
no code implementations • 11 May 2020 • David Suter, Ruwan Tennakoon, Erchuan Zhang, Tat-Jun Chin, Alireza Bab-Hadiashar
This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem.