Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning
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 object detector must detect OOD objects by localizing and correctly classifying them as OOD. Existing approaches primarily focus on OOD detection for image classification or rely on auxiliary branches, and typically do not treat in-distribution (ID) and OOD objects in a unified manner. We present SynOE-OD, a Synthetic Outlier-Exposure-based Object Detection method, which uses text-guided inpainting and an open-vocabulary object detector to generate and verify synthe...
| Authors | Sadia Ilyas, Annika Mütze, Klaus Friedrichs et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 March 2026 |
| Last updated at source | 14 October 2026 |
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