Dec 29, 2023 · We propose a novel network called UNBDet (Uncertainty-Boxes Detection) for enhancing open-set object detection.
Nov 21, 2024 · Our experimental results demonstrate that UNBDet significantly outperforms state-of-the-art models in open-set object detection. ResearchGate ...
Nov 12, 2021 · We propose GMM-. Det, a real-time method for extracting epistemic uncertainty from object detectors to identify and reject open-set errors. GMM- ...
Missing: Enhancing | Show results with:Enhancing
Computing the maximum and minimum coordinates allows us to establish bounding boxes for the object. Following this, through a filtering process, we generate ...
Nov 4, 2024 · A novel approach called PUDet (Pseudo-unknown Uncertainty Detector) based on Evidential Deep Learning (EDL) is proposed, incorporating two modules.
Dec 12, 2024 · We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images.
Feb 1, 2023 · We propose an active learning method for object detection using evidential deep learning and novel uncertainty aggregation method.
Missing: Identification. | Show results with:Identification.
Nov 11, 2022 · In this paper, we propose an uncertainty-aware open-set object detection framework based on faster R-CNN.
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In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination ...
Abstract—Accurate detection of objects from LiDAR point clouds is crucial for autonomous driving and environment modeling. However, uncertainties in ground ...