Ev-YOLO: Uncertainty-Aware Object Detection via a Unified Evidential Formulation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Reliable uncertainty estimation is essential for deploying object detectors in autonomous systems operating in uncertain environments. Evidential Deep Learning (EDL) provides a principled framework fo...
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: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:2605. 26477v2 Announce Type: replace Abstract: While Deep Neural Networks (DNNs) achieve remarkable performance, their tendency to produce overconfident predictions.
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.