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Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation
  Networks

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks

12 November 2019
Kira Maag
Matthias Rottmann
Hanno Gottschalk
ArXivPDFHTML

Papers citing "Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks"

14 / 14 papers shown
Title
Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration
Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration
D. Wolf
Alexander Braun
Markus Ulrich
89
0
0
18 Dec 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
32
0
0
17 Sep 2024
Decoupling of neural network calibration measures
Decoupling of neural network calibration measures
D. Wolf
Prasannavenkatesh Balaji
Alexander Braun
Markus Ulrich
UQCV
36
3
0
04 Jun 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
57
5
0
29 May 2024
Two Video Data Sets for Tracking and Retrieval of Out of Distribution
  Objects
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
Kira Maag
Robin Shing Moon Chan
Svenja Uhlemeyer
K. Kowol
Hanno Gottschalk
27
19
0
05 Oct 2022
False Negative Reduction in Semantic Segmentation under Domain Shift
  using Depth Estimation
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation
Kira Maag
Matthias Rottmann
16
3
0
07 Jul 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
21
0
0
30 May 2022
False Positive Detection and Prediction Quality Estimation for LiDAR
  Point Cloud Segmentation
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
Pascal Colling
Matthias Rottmann
L. Roese-Koerner
Hanno Gottschalk
3DPC
22
3
0
29 Oct 2021
False Negative Reduction in Video Instance Segmentation using
  Uncertainty Estimates
False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates
Kira Maag
UQCV
13
6
0
28 Jun 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 Trends
Q. Rahman
Peter Corke
Feras Dayoub
OOD
27
51
0
05 Jan 2021
Improving Video Instance Segmentation by Light-weight Temporal
  Uncertainty Estimates
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
Kira Maag
Matthias Rottmann
Serin Varghese
Fabian Hüger
Peter Schlicht
Hanno Gottschalk
UQCV
14
12
0
14 Dec 2020
Entropy Maximization and Meta Classification for Out-Of-Distribution
  Detection in Semantic Segmentation
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation
Robin Shing Moon Chan
Matthias Rottmann
Hanno Gottschalk
OODD
20
149
0
09 Dec 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
270
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
261
9,134
0
06 Jun 2015
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