641 citations · 1.1k across the 10 of their papers we have counts for
10 papers
Fi-ImageNet-1k: An OOD Benchmark From the Inside of the ImageNet-1k Validation Set
Ruslan Rozumnyi, Matěj Suchánek, Tomáš Vojíř +2
Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need images from outside the in-distr…
A Dataset for Semantic Segmentation in the Presence of Unknowns
Zakaria Laskar, Tomas Vojir, Matej Grcic +6
Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalie…
Calibrated Out-of-Distribution Detection with a Generic Representation
Tomas Vojir, Jan Sochman, Rahaf Aljundi +1
Out-of-distribution detection is a common issue in deploying vision models in practice and solving it is an essential building block in safety critical applications. Most of the ex…
Large Scale Joint Semantic Re-Localisation and Scene Understanding via Globally Unique Instance Coordinate Regression
Ignas Budvytis, Marvin Teichmann, Tomas Vojir +1
In this work we present a novel approach to joint semantic localisation and scene understanding. Our work is motivated by the need for localisation algorithms which not only predic…
Performance Evaluation Methodology for Long-Term Visual Object Tracking
Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…
Now you see me: evaluating performance in long-term visual tracking
Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2
We propose a new long-term tracking performance evaluation methodology and present a new challenging dataset of carefully selected sequences with many target disappearances. We per…
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…
Discriminative Correlation Filter with Channel and Spatial Reliability
Alan Lukežič, Tomáš Vojíř, Luka Čehovin +2
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial rel…
Online Adaptive Hidden Markov Model for Multi-Tracker Fusion
Tomas Vojir, Jiri Matas, Jana Noskova
In this paper, we propose a novel method for visual object tracking called HMMTxD. The method fuses observations from complementary out-of-the box trackers and a detector by utiliz…
A Novel Performance Evaluation Methodology for Single-Target Trackers
Matej Kristan, Jiri Matas, Ales Leonardis +6
This paper addresses the problem of single-target tracker performance evaluation. We consider the performance measures, the dataset and the evaluation system to be the most importa…