arXiv cs.CV
· Papers
Class Geometry as Supervision for Sample-Efficient Open-World Detection
arXiv:2608.12698v1 Announce Type: new Abstract: Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is especially challenging in scarce-data settings such as biomedical and scientific imaging, where rare categories may have on