arXiv:2609.24668v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying object detectors in autonomous systems operating in uncertain environments. Evidential Deep...
arXiv:2610.01409v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying object detectors when distribution/covariate shift and adversarial attacks may occur. Existi...
arXiv:2606. 28416v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions.
arXiv:2605. 26477v2 Announce Type: replace Abstract: While Deep Neural Networks (DNNs) achieve remarkable performance, their tendency to produce overconfident predictions.
arXiv:2603. 03043v3 Announce Type: replace-cross Abstract: While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex non-linear coordinate transformations and Intersection-over-Union (IoU) metrics.
arXiv:2602. 01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.
Generative Verification introduces an active learning strategy for object detection that uses an independent generative model to re‑derive a detection’s label from the pixels inside its predicted box. The disagreement between the detector’s label and the verifier’s label serves as the acquisition signal, automatically combining localization and classification errors into a single scalar and eliminating the need for hand‑weighted terms. Experiments on PASCAL VOC and MS‑COCO show that this signal outperforms traditional uncertainty and ensemble criteria, improving mAP50 by about one point per round, especially in early rounds where confident detector errors are most common.
arXiv:2606. 15767v1 Announce Type: cross Abstract: Understanding when and why deep neural networks are uncertain is crucial for deploying reliable machine learning systems in safety-critical domains.
The paper investigates the use of evidential deep learning (EDL) for multi‑modal anti‑UAV detection, comparing it with sigmoid baselines, Dempster‑Shafer evidence fusion, and uncertainty‑driven temporal sensor gating across three benchmarks (thermal tracking, RGB‑audio‑RF classification, and RGB‑IR tracking). EDL improves accuracy by up to 5.9 percentage points and better ranks classification errors, while the other components (DS fusion, Dirichlet vacuity, temporal gating) do not provide the expected benefits. The study concludes that the primary advantage of EDL stems from its training objective rather than its uncertainty estimates.
arXiv:2606. 04656v1 Announce Type: cross Abstract: Object detection is a safety-critical component of autonomous driving.
arXiv:2211.04340v2 Announce Type: replace Abstract: Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception...
arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).