VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection
Real-world weather, illumination, and imaging variations often induce severe domain shifts, degrading single-source detectors in unseen environments. Existing single-domain generalized object detection (SDGOD) methods mainly rely on data augmentation or domain-invariant learning, while largely overlooking how domain shift disrupts detector prediction stability. Through analytical experiments, we find that performance degradation is mainly dominated by increasing missed detections. Further analysis shows that this phenomenon stems from reduced cross-domain stability in DETR-style detectors: domain shift disrupts encoder-side object-background and inter-instance relations, and further weake...
| Authors | Yupeng Zhang, Ruize Han, Ningnan Guo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 April 2026 |
| Last updated at source | 22 May 2026 |
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