# Multivariate Confidence Calibration for Object Detection

arXiv: https://arxiv.org/abs/2004.13546 · HTML: https://mlsanity.com/paper/2004.13546/

## Key facts

- Cited by 98 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 98)

- [Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details](https://mlsanity.com/paper/2102.01066/) (2021)
- [Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future](https://mlsanity.com/paper/2206.02280/) (2022)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [TeST: Test-time Self-Training under Distribution Shift](https://mlsanity.com/paper/2209.11459/) (2022)
- [Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment](https://mlsanity.com/paper/2405.14497/) (2024)
- [Towards Improving Calibration in Object Detection Under Domain Shift](https://mlsanity.com/paper/2209.07601/) (2022)
- [Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2110.00249/) (2021)
- [Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction](https://mlsanity.com/paper/2403.07263/) (2024)
- [Task-Aware Information Routing from Common Representation Space in Lifelong Learning](https://mlsanity.com/paper/2302.11346/) (2023)
- [Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration](https://mlsanity.com/paper/2307.00934/) (2023)
- [ClassActionPrediction: A Challenging Benchmark for Legal Judgment Prediction of Class Action Cases in the US](https://mlsanity.com/paper/2211.00582/) (2022)
- [MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection](https://mlsanity.com/paper/2309.14976/) (2023)
- [Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors](https://mlsanity.com/paper/2107.04517/) (2021)
- [Consistency is the key to further mitigating catastrophic forgetting in continual learning](https://mlsanity.com/paper/2207.04998/) (2022)
- [Multiclass Confidence and Localization Calibration for Object Detection](https://mlsanity.com/paper/2306.08271/) (2023)
- [Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach](https://mlsanity.com/paper/2207.06267/) (2022)
- [Are we certain it's anomalous?](https://mlsanity.com/paper/2211.09224/) (2022)
- [Connecting Low-Loss Subspace for Personalized Federated Learning](https://mlsanity.com/paper/2109.07628/) (2021)
- [A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey](https://mlsanity.com/paper/2208.10895/) (2022)
- [Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection](https://mlsanity.com/paper/2303.14404/) (2023)
- [Cal-DETR: Calibrated Detection Transformer](https://mlsanity.com/paper/2311.03570/) (2023)
- [Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding](https://mlsanity.com/paper/2403.17010/) (2024)
- [On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines](https://mlsanity.com/paper/2405.20459/) (2024)
- [Confidence Calibration of Classifiers with Many Classes](https://mlsanity.com/paper/2411.02988/) (2024)
- [Calibrated Teacher for Sparsely Annotated Object Detection](https://mlsanity.com/paper/2303.07582/) (2023)
- [Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection](https://mlsanity.com/paper/2207.01242/) (2022)
- [Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection](https://mlsanity.com/paper/2312.06645/) (2023)
- [From Black-box to White-box: Examining Confidence Calibration under different Conditions](https://mlsanity.com/paper/2101.02971/) (2021)
- [A Comprehensive Study of Vision Transformers on Dense Prediction Tasks](https://mlsanity.com/paper/2201.08683/) (2022)

## Abstract (arXiv)

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.