GroupEnsemble: Efficient Uncertainty Estimation for DETR-based Object Detection

• URL: http://arxiv.org/abs/2603.01847v1
• Date: Mon, 02 Mar 2026 13:26:40 GMT
• Title: GroupEnsemble: Efficient Uncertainty Estimation for DETR-based Object Detection
• Authors: Yutong Yang, Katarina Popović, Julian Wiederer, Markus Braun, Vasileios Belagiannis, Bin Yang,
• Abstract summary: GroupEnsemble is an efficient and effective uncertainty estimation method for DETR-like models.<n>We validated our method under autonomous driving scenes and common daily scenes using the Cityscapes and COCO datasets.<n>The results show that a hybrid approach combining MC-Dropout and GroupEnsemble outperforms Deep Ensembles on several metrics at a fraction of the cost.
• Score: 10.241307426902178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detection Transformer (DETR) and its variants show strong performance on object detection, a key task for autonomous systems. However, a critical limitation of these models is that their confidence scores only reflect semantic uncertainty, failing to capture the equally important spatial uncertainty. This results in an incomplete assessment of the detection reliability. On the other hand, Deep Ensembles can tackle this by providing high-quality spatial uncertainty estimates. However, their immense memory consumption makes them impractical for real-world applications. A cheaper alternative, Monte Carlo (MC) Dropout, suffers from high latency due to the need of multiple forward passes during inference to estimate uncertainty. To address these limitations, we introduce GroupEnsemble, an efficient and effective uncertainty estimation method for DETR-like models. GroupEnsemble simultaneously predicts multiple individual detection sets by feeding additional diverse groups of object queries to the transformer decoder during inference. Each query group is transformed by the shared decoder in isolation and predicts a complete detection set for the same input. An attention mask is applied to the decoder to prevent inter-group query interactions, ensuring each group detects independently to achieve reliable ensemble-based uncertainty estimation. By leveraging the decoder's inherent parallelism, GroupEnsemble efficiently estimates uncertainty in a single forward pass without sequential repetition. We validated our method under autonomous driving scenes and common daily scenes using the Cityscapes and COCO datasets, respectively. The results show that a hybrid approach combining MC-Dropout and GroupEnsemble outperforms Deep Ensembles on several metrics at a fraction of the cost. The code is available at https://github.com/yutongy98/GroupEnsemble.

Related papers

• SetAD: Semi-Supervised Anomaly Learning in Contextual Sets [25.628827917857603]
  Semi-supervised anomaly detection has shown great promise by effectively leveraging limited labeled data.<n>We propose SetAD, a novel framework that reframes semi-supervised AD as a Set-level Anomaly Detection task.<n>To enhance robustness and score calibration, we propose a context-calibrated anomaly scoring mechanism.
  arXiv  Detail & Related papers  (2025-11-26T13:27:59Z)
• FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation [4.544160712377809]
  This work presents a novel federated learning approach to achieve universal segmentation across diverse abdominal CT datasets.<n>The proposed method quantifies prediction uncertainty by propagating the uncertainty from the model weights.<n> Experimental evaluations demonstrate the effectiveness of this approach in improving the quality of federated aggregation and uncertainty-weighted inference.
  arXiv  Detail & Related papers  (2025-08-08T11:34:01Z)
• An Uncertainty-aware DETR Enhancement Framework for Object Detection [10.102900613370817]
  We propose an uncertainty-aware enhancement framework for DETR-based object detectors.<n>We derive a Bayes Risk formulation to filter high-risk information and improve detection reliability.<n> Experiments on the COCO benchmark show that our method can be effectively integrated into existing DETR variants.
  arXiv  Detail & Related papers  (2025-07-20T07:53:04Z)
• Uncertainty-Aware Prototype Semantic Decoupling for Text-Based Person Search in Full Images [16.071286104558393]
  Text-based pedestrian search (TBPS) in full images aims to locate a target pedestrian in untrimmed images using natural language descriptions.<n>We propose UPD-TBPS, a novel framework comprising three modules: Multi-granularity Uncertainty Estimation (MUE), Prototype-based Uncertainty Decoupling (PUD), and Cross-modal Re-identification (ReID)
  arXiv  Detail & Related papers  (2025-05-06T14:25:30Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient ensembling method for self-attention networks.<n>The method not only outperforms state-of-the-art implicit techniques like BatchEnsemble, but even matches or exceeds the accuracy of an Explicit Ensemble.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Uncertainty-aware Unsupervised Multi-Object Tracking [33.53331700312752]
  unsupervised multi-object trackers are inferior to learning reliable feature embeddings. Recent self-supervised techniques are adopted, whereas they failed to capture temporal relations. This paper argues that though the uncertainty problem is inevitable, it is possible to leverage the uncertainty itself to improve the learned consistency in turn.
  arXiv  Detail & Related papers  (2023-07-28T09:03:06Z)
• Semi-DETR: Semi-Supervised Object Detection with Detection Transformers [105.45018934087076]
  We analyze the DETR-based framework on semi-supervised object detection (SSOD) We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector. Our method outperforms all state-of-the-art methods by clear margins.
  arXiv  Detail & Related papers  (2023-07-16T16:32:14Z)
• 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)
• 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)
• Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections [50.096540945099704]
  We propose a novel ensemble called the Probabilistic Ranking Aware Ensemble (PRAE) that refines the confidence of bounding boxes from detectors. We also introduce a bandit approach to address the confidence imbalance problem caused by the need to deal with different numbers of boxes.
  arXiv  Detail & Related papers  (2021-05-07T09:37:06Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

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.