OWOD-FSL: Open-World Object Detection Via Few-Shot Learning and Dynamic Prototypes

Abstract

Open-World Object Detection (OWOD) presents a critical challenge for modern computer vision systems: detecting known classes, identifying unknown objects, and incrementally learning to recognize them over time. However, current approaches have two fundamental limitations: (1) the fixed-dimensional classification head inherently restricts incremental learning capabilities, and (2) heavy reliance on extensive annotated data hinders adaptability in few-shot settings. To address these limitations, we propose OWOD-FSL that integrates dynamic prototype classification head with few-shot learning. At the core of our approach are two major contributions: a dynamic prototype classification head that supplants traditional fixed classifiers with an expandable prototype classifier for unlimited class expansion, and a biologically-inspired bi-phase learning strategy that integrates offline prototype generation with incremental learning refinement. Comprehensive experiments on M-OWODB benchmark shows that OWOD-FSL achieves state-of-the-art performance in both unknown class recall (U-Recall) and known class mAP, significantly outperforming existing methods.