Instance-Level Post Hoc Uncertainty Quantification in Object Detection

• URL: http://arxiv.org/abs/2606.04656v1
• Date: Wed, 03 Jun 2026 09:26:13 GMT
• Title: Instance-Level Post Hoc Uncertainty Quantification in Object Detection
• Abstract summary: It is essential to quantify the uncertainty in bounding-box predictions for safety assurance.<n>We propose Monte-Carlo generalized linearized model (MC-GLM)<n>The number of samples required in the Monte Carlo step is constant and independent of the number of output instances, so it can be parallelized.
• Score: 7.463898712296942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection is a safety-critical component of autonomous driving. It is essential to quantify the uncertainty in bounding-box predictions for safety assurance. Post hoc uncertainty quantification without retraining aligns with real-world deployment requirements; therefore, we employ the Laplace approximation. Because instance-level uncertainty is needed, linearized inference methods that require multiple backpropagations are not time-efficient, and sampling-based methods are not fully post hoc. We propose Monte-Carlo generalized linearized model (MC-GLM), which provides instance-level and approximately post hoc uncertainty quantification. The number of samples required in the Monte Carlo step is constant and independent of the number of output instances, so it can be parallelized. Experiments on the nuScenes dataset with the CenterPoint detector validate the effectiveness of our method, and the resulting uncertainties exhibit good quality.

Related papers

• From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty [9.798854302346802]
  Large Language Models (LLMs) that can express interpretable and calibrated uncertainty are crucial in high-stakes domains.<n>We propose a three-stage pipeline to post-train LLMs to efficiently infer calibrated uncertainty estimates for their responses.<n> Experiments show that models trained with our pipeline achieve better calibration than baselines and generalize to unseen tasks without further processing.
  arXiv  Detail & Related papers  (2026-03-06T14:21:42Z)
• Multidimensional Uncertainty Quantification via Optimal Transport [87.97146725546502]
  We take a multidimensional view on uncertainty quantification (UQ) by stacking complementary UQ measures into a vector.<n>VecUQ-OT shows high efficiency even when individual measures fail.
  arXiv  Detail & Related papers  (2025-09-26T14:09:03Z)
• Efficient Uncertainty Estimation via Distillation of Bayesian Large Language Models [12.69571386421462]
  In this paper, we investigate the possibility of eliminating the need for test-time sampling for uncertainty estimation.<n>We distill an off-the-shelf Bayesian LLM into a non-Bayesian student LLM by minimizing the divergence between their predictive distributions.<n>Our experiments demonstrate that uncertainty estimation capabilities on training data can successfully generalize to unseen test data.
  arXiv  Detail & Related papers  (2025-05-16T22:26:03Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Uncertainty Quantification of Collaborative Detection for Self-Driving [12.590332512097698]
  Sharing information between connected and autonomous vehicles (CAVs) improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges. Our work is the first to estimate the uncertainty of collaborative object detection.
  arXiv  Detail & Related papers  (2022-09-16T20:30:45Z)
• BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks [50.15201777970128]
  We propose BayesCap that learns a Bayesian identity mapping for the frozen model, allowing uncertainty estimation. BayesCap is a memory-efficient method that can be trained on a small fraction of the original dataset. We show the efficacy of our method on a wide variety of tasks with a diverse set of architectures.
  arXiv  Detail & Related papers  (2022-07-14T12:50:09Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.