activity
20132023
most citedA Novel Performance Evaluation Methodology for Single-Target Trackers

641 citations · 3k across the 58 of their papers we have counts for

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Showing 2017 · cs.CVShow all

7 papers · 2 filters

cs.CV2017★ 1 cited

FuCoLoT -- A Fully-Correlational Long-Term Tracker

Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2

We propose FuCoLoT -- a Fully Correlational Long-term Tracker. It exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the targe…

cs.CV2017

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych +2

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art perf…

cs.CV2017

Repeatability Is Not Enough: Learning Affine Regions via Discriminability

Dmytro Mishkin, Filip Radenovic, Jiri Matas

A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably…

cs.CV2017

Graph-Cut RANSAC

Daniel Barath, Jiri Matas

A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local opt…

cs.CV2017

Multi-Class Model Fitting by Energy Minimization and Mode-Seeking

Daniel Barath, Jiri Matas

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting - the problem of interpreting the input data as a mixture of noisy observations origi…

cs.CV2017★ 298 cited

Working hard to know your neighbor's margins: Local descriptor learning loss

Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic +1

We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT. We show that the proposed loss that maximizes the dist…

cs.CV2017★ 18 cited

T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects

Tomas Hodan, Pavel Haluza, Stepan Obdrzalek +3

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant ob…