English(EN) Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning

新方法改进街景中的分布外目标检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了SynOE-OD，一种用于街景中分布外（OOD）目标检测的新颖方法。该方法利用文本引导的图像修复和开放词汇目标检测器来离线创建和验证合成异常值。然后，利用这些合成异常值对现有目标检测器进行微调，在推理时无需辅助分支即可纳入OOD类别。SynOE-OD在OoDIS基准测试中展示了最先进的性能，在RoadObstacle、RoadAnomaly和Fishyscapes等数据集上优于之前的基线，同时保持了具有竞争力的分布内检测能力。 AI

影响通过使目标检测系统能够识别和分类以前未见过的物体，增强了其可靠性，这对于自主系统至关重要。

排序理由这是一篇详细介绍目标检测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Sadia Ilyas, Annika M\"utze, Klaus Friedrichs, Thomas Kurbiel, Matthias Rottmann · 2026-10-16 04:00

通过合成异常值暴露和迁移学习实现街景中的分布外目标检测

arXiv:2603.16122v2 Announce Type: replace Abstract: Out-of-distribution (OOD) objects are often silently overlooked by object detectors, which produce no detection for them, leaving downstream modules with nothing to act on and no indication that anything was missed. A reliable o…

本文如何被排名

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通过合成异常值暴露和迁移学习实现街景中的分布外目标检测

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