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MetaDetect: Uncertainty Quantification and Prediction Quality Estimates
  for Object Detection
v1v2 (latest)

MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

IEEE International Joint Conference on Neural Network (IJCNN), 2020
4 October 2020
Marius Schubert
Karsten Kahl
Matthias Rottmann
    UQCV
ArXiv (abs)PDFHTML

Papers citing "MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection"

20 / 20 papers shown
Title
From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets
From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets
Sarina Penquitt
Jonathan Klees
Rinor Cakaj
Daniel Kondermann
Matthias Rottmann
Lars Schmarje
111
1
0
06 Aug 2025
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
Thomas Gottwald
Edgar Heinert
Peter Stehr
Chamuditha Jayanga Galappaththige
Matthias Rottmann
3DGSUQCV
360
0
0
04 Aug 2025
Conformal Object Detection by Sequential Risk Control
Conformal Object Detection by Sequential Risk Control
Léo Andéol
Luca Mossina
Adrien Mazoyer
Sébastien Gerchinovitz
345
0
0
29 May 2025
Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -
Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -
Laura Fieback
Nishilkumar Balar
Jakob Spiegelberg
Hanno Gottschalk
MLLMVLM
343
0
0
16 Apr 2025
Machine vision-aware quality metrics for compressed image and video
  assessment
Machine vision-aware quality metrics for compressed image and video assessmentInternational Conference on Pattern Recognition (ICPR), 2024
M. Dremin
Konstantin Kozhemyakov
Ivan Molodetskikh
Malakhov Kirill
Artur Sagitov
D. Vatolin
173
1
0
11 Nov 2024
Uncertainty and Prediction Quality Estimation for Semantic Segmentation
  via Graph Neural Networks
Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks
Edgar Heinert
Stephan Tilgner
Timo Palm
Matthias Rottmann
UQCV
328
1
0
17 Sep 2024
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification
Laura Fieback
Jakob Spiegelberg
Hanno Gottschalk
MLLM
438
7
0
29 May 2024
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble
  Architecture
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble ArchitectureBritish Machine Vision Conference (BMVC), 2023
S. Qutub
Neslihan Kose
Rafael Rosales
Michael Paulitsch
Korbinian Hagn
Florian Geissler
Yang Peng
Gereon Hinz
Alois C. Knoll
409
3
0
14 Sep 2023
LMD: Light-weight Prediction Quality Estimation for Object Detection in
  Lidar Point Clouds
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
Tobias Riedlinger
Marius Schubert
Sarina Penquitt
Jan-Marcel Kezmann
Pascal Colling
Karsten Kahl
L. Roese-Koerner
Michael Arnold
Urs Zimmermann
Matthias Rottmann
3DPC
209
3
0
13 Jun 2023
Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel
  Estimation in 5G Networks
Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G NetworksMediterranean Conference on Embedded Computing (MECO), 2023
Ferhat Ozgur Catak
Marc Brittain
Murat Kuzlu
Christine Serres
UQCV
115
2
0
04 May 2023
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks
  applied to Out-of-Distribution Segmentation
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation
Kira Maag
Tobias Riedlinger
UQCV
207
10
0
13 Mar 2023
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical
  2D Object Detection with Margin Entropy Loss
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss
Yannik Blei
Nicolas Jourdan
Nils Gählert
OODD
122
4
0
01 Sep 2022
Uncertainty Quantification and Resource-Demanding Computer Vision
  Applications of Deep Learning
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning
Julian Burghoff
Robin Shing Moon Chan
Hanno Gottschalk
Annika Muetze
Tobias Riedlinger
Matthias Rottmann
Marius Schubert
BDL
159
1
0
30 May 2022
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent
  Advances and Applications
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
Xinlei Zhou
Han Liu
Farhad Pourpanah
T. Zeng
Xizhao Wang
UQCVUD
302
73
0
03 Nov 2021
Post-hoc Models for Performance Estimation of Machine Learning Inference
Post-hoc Models for Performance Estimation of Machine Learning Inference
Xuechen Zhang
Samet Oymak
Jiasi Chen
UQCV
161
7
0
06 Oct 2021
Prediction Surface Uncertainty Quantification in Object Detection Models
  for Autonomous Driving
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving
Ferhat Ozgur Catak
T. Yue
Shaukat Ali
128
25
0
11 Jul 2021
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object
  Detectors
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object DetectorsIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2021
Tobias Riedlinger
Matthias Rottmann
Marius Schubert
Hanno Gottschalk
BDLUQCV
270
15
0
09 Jul 2021
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey
  of Emerging Trends
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging TrendsIEEE Access (IEEE Access), 2021
Q. Rahman
Peter Corke
Feras Dayoub
OOD
441
64
0
05 Jan 2021
Improving Video Instance Segmentation by Light-weight Temporal
  Uncertainty Estimates
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty EstimatesIEEE International Joint Conference on Neural Network (IJCNN), 2020
Kira Maag
Matthias Rottmann
Serin Varghese
Fabian Hüger
Peter Schlicht
Hanno Gottschalk
UQCV
215
13
0
14 Dec 2020
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera
  and Radar Sensors
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
K. Kowol
Matthias Rottmann
S. Bracke
Hanno Gottschalk
UQCV
211
41
0
07 Oct 2020
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