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

Open-world object detection requires knowledge of categories and discovery of new objects never seen in training. Full model fine tuning fails often in such dynamic environment. Fully tuned models overfit known classes and lose their sensitivity to unknown objects at high computational cost. To solve this problem, we propose a Parameter-Efficient Open-World Object Detection (PE-OWOD) framework, a simple and light approach to retrain. Instead of updating the whole network, we lock backbone and encoder to maintain stable visual priors and inject compact Residual adapters only into decoder to adapt tasks. We also introduce VOS, which defines explicit decision boundary for open space with optional semantic initialization. MS-COCO benchmarks show remarkable efficiency advantages: Update less than 27% of models, PE-OWOD achieves 64.7% Unknown Recall (significantly outperform fully tuned baselines), and GPU memory usage is reduced by 86%. These results indicate that effective parameter adaptation is not a constraint; rather, it is a reliable and robust open-world detection strategy.