Uncertainty Estimation For Object Detection

Media Summary: An overview of my dissertation. A reproduce of the public talk as part of my PhD defense, which took place online on June 18th, ... Authors: Jiawei Liu (Australian National University)*; Jing Zhang (Australian National University); Nick Barnes (ANU) Description: ... In this work, we introduce a new technique that combines two popular methods to estimate

Uncertainty Estimation For Object Detection - Detailed Analysis & Overview

An overview of my dissertation. A reproduce of the public talk as part of my PhD defense, which took place online on June 18th, ... Authors: Jiawei Liu (Australian National University)*; Jing Zhang (Australian National University); Nick Barnes (ANU) Description: ... In this work, we introduce a new technique that combines two popular methods to estimate Published at ICRA 2022 ( In this work, We propose f-Cal, a variational calibration method to obtain ... Authors: Riedlinger, Tobias*; Rottmann, Matthias; Schubert, Marius; Gottschalk, Hanno Description: The majority of In this work, we present Masksembles, a novel method for generating

Neural networks predictions are unreliable when the input sample is out of the training data distribution or corrupted by noise. BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022 Top: prediction with class: score / predicted iou Bottim: prediction with class: score / true iou The color inside the boxes is green for ...

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Uncertainty Estimation for Object Detection Using Deep Learning Approaches

Modeling Aleatoric Uncertainty for Camouflaged Object Detection

Ivan Provilkov: Tutorial on Uncertainty Estimation

An Uncertainty Estimation Framework for Probabilistic Object Detection

250 - Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distil

f-Cal - Aleatoric uncertainty quantification for robot perception via calibrated neural regression

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

[CVPR] Masksembles for Uncertainty Estimation

Uncertainty Estimation in ML Track Premiere

A General Framework for Uncertainty Estimation in Deep Learning

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

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Uncertainty Estimation for Object Detection Using Deep Learning Approaches

Uncertainty Estimation for Object Detection Using Deep Learning Approaches

An overview of my dissertation. A reproduce of the public talk as part of my PhD defense, which took place online on June 18th, ...

Modeling Aleatoric Uncertainty for Camouflaged Object Detection

Modeling Aleatoric Uncertainty for Camouflaged Object Detection

Authors: Jiawei Liu (Australian National University)*; Jing Zhang (Australian National University); Nick Barnes (ANU) Description: ...

Ivan Provilkov: Tutorial on Uncertainty Estimation

Ivan Provilkov: Tutorial on Uncertainty Estimation

Data Fest Online 2020

An Uncertainty Estimation Framework for Probabilistic Object Detection

An Uncertainty Estimation Framework for Probabilistic Object Detection

In this work, we introduce a new technique that combines two popular methods to estimate

250 - Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distil

250 - Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distil

We introduce real-time

f-Cal - Aleatoric uncertainty quantification for robot perception via calibrated neural regression

f-Cal - Aleatoric uncertainty quantification for robot perception via calibrated neural regression

Published at ICRA 2022 (https://icra2022.org/), In this work, We propose f-Cal, a variational calibration method to obtain ...

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Authors: Riedlinger, Tobias*; Rottmann, Matthias; Schubert, Marius; Gottschalk, Hanno Description: The majority of

[CVPR] Masksembles for Uncertainty Estimation

[CVPR] Masksembles for Uncertainty Estimation

In this work, we present Masksembles, a novel method for generating

Uncertainty Estimation in ML Track Premiere

Uncertainty Estimation in ML Track Premiere

Data Fest Online 2020

A General Framework for Uncertainty Estimation in Deep Learning

A General Framework for Uncertainty Estimation in Deep Learning

Neural networks predictions are unreliable when the input sample is out of the training data distribution or corrupted by noise.

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

BayesOD A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection, CoRL2022

MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection

Top: prediction with class: score / predicted iou Bottim: prediction with class: score / true iou The color inside the boxes is green for ...