no code implementations • 10 Jul 2022 • Kha Gia Quach, Huu Le, Pha Nguyen, Chi Nhan Duong, Tien Dai Bui, Khoa Luu
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions.
no code implementations • 21 Feb 2022 • Giang Truong, Huu Le, Alvaro Parra, Syed Zulqarnain Gilani, Syed M. S. Islam, David Suter
The volume of data to handle, and still elusive need to have the registration occur fully reliably and fully automatically, mean there is a need to innovate further.
no code implementations • CVPR 2022 • Huu Le, Rasmus Kjær Høier, Che-Tsung Lin, Christopher Zach
We propose a new algorithm for training deep neural networks (DNNs) with binary weights.
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 • Kha Gia Quach, Pha Nguyen, Huu Le, Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Khoa Luu
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
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 • 22 Feb 2021 • Huu Le, Christopher Zach
Robust parameter estimation is a crucial task in several 3D computer vision pipelines such as Structure from Motion (SfM).
1 code implementation • 21 Oct 2020 • Huu Le, Christopher Zach, Edward Rosten, Oliver J. Woodford
Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems.
1 code implementation • CVPR 2020 • Huu Le, Christopher Zach
Due to the highly non-convex nature of large-scale robust parameter estimation, avoiding poor local minima is challenging in real-world applications where input data is contaminated by a large or unknown fraction of outliers.
no code implementations • ECCV 2020 • Christopher Zach, Huu Le
Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning.
1 code implementation • ICCV 2019 • Huu Le, Ming Xu, Tuan Hoang, Michael Milford
We benchmark the performance of the proposed algorithm on several real-world benchmark datasets and experimentally validate the theoretical sub-linearity of our approach, while also showing that our approach yields competitive absolute storage performance as well.
no code implementations • 27 Jun 2019 • Huu Le, Tuan Hoang, Michael Milford
Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques.
no code implementations • 24 Apr 2019 • Thanh-Toan Do, Khoa Le, Tuan Hoang, Huu Le, Tam V. Nguyen, Ngai-Man Cheung
This global vector is then subjected to a hashing function to generate a binary hash code.
1 code implementation • 6 Apr 2019 • Huu Le, Thanh-Toan Do, Tuan Hoang, Ngai-Man Cheung
In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.
no code implementations • 5 Feb 2019 • Huu Le, Tuan Hoang, Qianggong Zhang, Thanh-Toan Do, Anders Eriksson, Michael Milford
In this paper, we present a novel 6-DOF localization system that for the first time simultaneously achieves all the three characteristics: significantly sub-linear storage growth, agnosticism to image descriptors, and customizability to available storage and computational resources.
1 code implementation • 24 Oct 2018 • Huu Le, Anders Eriksson, Thanh-Toan Do, Michael Milford
This approach allows us to solve constrained K-Means where multiple types of constraints can be simultaneously enforced.
1 code implementation • 23 Oct 2018 • Huu Le, Michael Milford
Robotic and animal mapping systems share many of the same objectives and challenges, but differ in one key aspect: where much of the research in robotic mapping has focused on solving the data association problem, the grid cell neurons underlying maps in the mammalian brain appear to intentionally break data association by encoding many locations with a single grid cell neuron.
1 code implementation • ECCV 2018 • Zhipeng Cai, Tat-Jun Chin, Huu Le, David Suter
In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization.
no code implementations • 21 Feb 2018 • Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Trung Pham, Huu Le, Ngai-Man Cheung, Ian Reid
However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images.
no code implementations • 19 Feb 2018 • Tuan Hoang, Thanh-Toan Do, Huu Le, Dang-Khoa Le-Tan, Ngai-Man Cheung
For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss.
1 code implementation • 7 Feb 2018 • Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Huu Le, Tam V. Nguyen, Ngai-Man Cheung
In the large-scale image retrieval task, the two most important requirements are the discriminability of image representations and the efficiency in computation and storage of representations.
1 code implementation • 27 Oct 2017 • Huu Le, Tat-Jun Chin, Anders Eriksson, Thanh-Toan Do, David Suter
Further, our approach is naturally applicable to estimation problems with geometric residuals
no code implementations • CVPR 2017 • Huu Le, Tat-Jun Chin, David Suter
Our method is based on a formulating the problem with linear complementarity constraints, then defining a penalized version which is provably equivalent to the original problem.
no code implementations • CVPR 2016 • Huu Le, Tat-Jun Chin, David Suter
Deformations of surfaces with the same intrinsic shape can often be described accurately by a conformal model.