WS-iFSD: Weakly Supervised Incremental Few-Shot Object Detection Without Forgetting

Abstract

Traditional object detection algorithms rely on extensive annotations from a pre-defined set of base categories, leaving them ill-equipped to identify objects from novel classes. We address this limitation by introducing a novel framework for Incremental Few-Shot Object Detection (iFSD). Leveraging a meta-learning approach, our \hypernetwork is designed to generate class-specific codes, enabling object recognition from both base and novel categories. To enhance the \hypernetwork’s generalization performance, we propose a Weakly Supervised Class Augmentation technique that significantly amplifies the training data by merely requiring image-level labels for object localization. Additionally, we stabilize detection performance on base categories by freezing the backbone and detection heads during meta-training. Our model demonstrates significant performance gains on two major benchmarks. Specifically, it outperforms the state-of-the-art ONCE approach on the MS COCO dataset by margins of $2.8%$ and $20.5%$ in box AP for novel and base categories, respectively. When trained on MS COCO and cross-evaluated on PASCAL VOC, our model achieves a four-fold improvement in box AP compared to ONCE.