Search Results for author:
Found 24 papers, 11 papers with code
Depth Perspective-aware Multiple Object Tracking
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.
Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration
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.
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
We propose a new algorithm for training deep neural networks (DNNs) with binary weights.
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach
In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting.
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach
In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting.
Escaping Poor Local Minima in Large Scale Robust Estimation
Robust parameter estimation is a crucial task in several 3D computer vision pipelines such as Structure from Motion (SfM).
Progressive Batching for Efficient Non-linear Least Squares
Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems.
A Graduated Filter Method for Large Scale Robust Estimation
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.
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning
Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning.
Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost
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.
BTEL: A Binary Tree Encoding Approach for Visual Localization
Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques.
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning
This global vector is then subjected to a hashing function to generate a binary hash code.
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences
In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences.
SASSE: Scalable and Adaptable 6-DOF Pose Estimation
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.
A Binary Optimization Approach for Constrained K-Means Clustering
This approach allows us to solve constrained K-Means where multiple types of constraints can be simultaneously enforced.
Large scale visual place recognition with sub-linear storage growth
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.
Deterministic consensus maximization with biconvex programming
In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization.
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
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.
Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing
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.
From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval
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.
Deterministic Approximate Methods for Maximum Consensus Robust Fitting
Further, our approach is naturally applicable to estimation problems with geometric residuals
An Exact Penalty Method for Locally Convergent Maximum Consensus
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.
Conformal Surface Alignment With Optimal Mobius Search
Deformations of surfaces with the same intrinsic shape can often be described accurately by a conformal model.
