Semantic priors and virtual outlier synthesis enable parameter-efficient open-world object detection

A Parameter-Efficient Open-World Object Detection (PE-OWOD) framework is proposed, a simple and light approach to retrain, and VOS, which defines explicit decision boundary for open space with optional semantic initialization is introduced.