21 citations · 41 across the 16 of their papers we have counts for
18 papers
Depth Perspective-aware Multiple Object Tracking
Kha Gia Quach, Huu Le, Pha Nguyen +3
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. Indee…
Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration
Giang Truong, Huu Le, Alvaro Parra +3
With current trends in sensors (cheaper, more volume of data) and applications (increasing affordability for new tasks, new ideas in what 3D data could be useful for); there is cor…
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
Huu Le, Rasmus Kjær Høier, Che-Tsung Lin +1
We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bi…
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Kha Gia Quach, Pha Nguyen, Huu Le +4
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number…
Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach
Giang Truong, Huu Le, David Suter +2
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, howe…
Escaping Poor Local Minima in Large Scale Robust Estimation
Huu Le, Christopher Zach
Robust parameter estimation is a crucial task in several 3D computer vision pipelines such as Structure from Motion (SfM). State-of-the-art algorithms for robust estimation, howeve…
Progressive Batching for Efficient Non-linear Least Squares
Huu Le, Christopher Zach, Edward Rosten +1
Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems. Most improvements of the basic Gauss-Newton algorithm tackle converg…
A Graduated Filter Method for Large Scale Robust Estimation
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 contaminate…
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning
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. The additional…
BTEL: A Binary Tree Encoding Approach for Visual Localization
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. However, there rema…
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning
Thanh-Toan Do, Khoa Le, Tuan Hoang +3
Representing images by compact hash codes is an attractive approach for large-scale content-based image retrieval. In most state-of-the-art hashing-based image retrieval systems, f…
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences
Huu Le, Thanh-Toan Do, Tuan Hoang +1
This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative corres…
SASSE: Scalable and Adaptable 6-DOF Pose Estimation
Huu Le, Tuan Hoang, Qianggong Zhang +3
Visual localization has become a key enabling component of many place recognition and SLAM systems. Contemporary research has primarily focused on improving accuracy and precision-…
A Binary Optimization Approach for Constrained K-Means Clustering
Huu Le, Anders Eriksson, Thanh-Toan Do +1
K-Means clustering still plays an important role in many computer vision problems. While the conventional Lloyd method, which alternates between centroid update and cluster assignm…
Large scale visual place recognition with sub-linear storage growth
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 solv…
Deterministic consensus maximization with biconvex programming
Zhipeng Cai, Tat-Jun Chin, Huu Le +1
Consensus maximization is one of the most widely used robust fitting paradigms in computer vision, and the development of algorithms for consensus maximization is an active researc…
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +4
Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task…
Deterministic Approximate Methods for Maximum Consensus Robust Fitting
Huu Le, Tat-Jun Chin, Anders Eriksson +2
Maximum consensus estimation plays a critically important role in robust fitting problems in computer vision. Currently, the most prevalent algorithms for consensus maximization dr…