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<!DOCTYPE html>
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<head>
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<meta name="description" content="Net:Cal - Multivariate Confidence Calibration for Object Detection">
<meta property="og:title" content="Multivariate Confidence Calibration for Object Detection"/>
<meta property="og:description" content="Presenting a framework to enhance accuracy in object detection through innovative calibration of confidence estimates, addressing a gap in current methods. Utilizing additional regression output information, our approach achieves calibrated confidence with respect to image location and box scale. We also introduce a new evaluation measure for detector miscalibration and demonstrate superior performance over existing models, ensuring reliable estimates across various conditions."/>
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<meta name="twitter:description" content="Introducing a novel framework to boost object detection accuracy by calibrating confidence estimates using regression data, filling a crucial gap. Achieves calibrated confidence for different image locations and scales, outperforming existing models." />
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<meta name="keywords" content="#CVPRW #SAIAD #ObjectDetection #AI #NetCal #UncertaintyQuantification #UncertaintyCalibration #ConfidenceCalibration">
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<title>Multivariate Confidence Calibration for Object Detection</title>
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<h1 class="title is-1 publication-title">Multivariate Confidence Calibration for Object Detection</h1>
<div class="is-size-5 publication-authors">
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<span class="author-block">
<a href="https://www.linkedin.com/in/fabian-küppers-726353201/" target="_blank">Fabian Küppers</a><sup>1</sup>,</span>
<span class="author-block">
<a href="" target="_blank">Jan Kronenberger</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/amirhossein-shantia-74b51625/" target="_blank">Amirhossein Shantia</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://trustin.ai" target="_blank">Anselm Haselhoff</a><sup>1</sup>
</span>
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<span class="author-block"><small><sup>1</sup>Ruhr West University of Applied Sciences Bottrop, Germany; <sup>2</sup>Visteon Electronics GmbH Kerpen, Germany</small></span>
<span class="author-block">Computer Vision and Pattern Recognition (CVPR) Workshops, 2020</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods. The main difference to related work in the field of classifier calibration is that we also use additional information of the regression output of an object detector for calibration. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. In addition, we propose a new measure to evaluate miscalibration of object detectors. Finally, we show that our developed methods outperform state-of-the-art calibration models for the task of object detection and provides reliable confidence estimates across different locations and scales.
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<img src="static/images/carousel1.png" alt="Multivariate calibration (bottom) vs. standard confidence calibration (top)."/>
<h2 class="subtitle has-text-centered">
Extended box-sensitive or multivariate calibration proposed in this paper (bottom) vs. standard confidence calibration (top). An object detector predicts a class with a certain confidence and a box proposal containing information about location and scale. Standard calibration methods only take the confidence into account. Our approach also includes the regression branch of an object detector in order to improve the calibration results.
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<img src="static/images/carousel2.png" alt="Dependent Logistic Calibration"/>
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Toy example of position dependent calibration.
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<pre><code>@InProceedings{Kuppers_2020_CVPR_Workshops,
author = {Küppers, Fabian and Kronenberger, Jan and Shantia, Amirhossein and Haselhoff, Anselm},
title = {Multivariate Confidence Calibration for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}</code></pre>
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