Fixing Background Misclassification in Few-Shot Object Detection via Product of Experts

Few-shot object detection (FSOD) poses a significant challenge due to the difficulty of learning robust and discriminative object representations under limited supervision. A widely adopted solution is the two-stage fine-tuning framework, wherein knowledge acquired from a large-scale base dataset is transferred to a novel dataset containing only a small number of labeled instances. However, this framework is prone to systematically misclassifying novel objects as background, primarily due to incorrect background label caused by the domain gap between base and novel datasets-an issue exacerbated by the sparse representation of novel categories. In this work, we show that this inherent weakness can be exploited by explicitly redefining the category structure and transferring the representations learned during the base training stage. Building on this insight, we propose a simple yet effective framework grounded in the Product of Experts (PoE) formulation, which estimates the joint distribution over background and novel categories by combining the unnormalized logits from independently trained classifiers. Notably, it does not require modifications of the base model or repetition of the base training phase. Furthermore, we introduce a strategy for identifying additional novel-category instances within the base dataset, which effectively augmenting the training set for fine-tuning. The resulting method is architecture-agnostic, imposes negligible overhead, and integrates seamlessly with existing two-stage fine-tuning pipelines. Extensive experiments on PASCAL VOC and COCO demonstrate that the proposed method yields consistent improvements across different baselines, achieving significant gains over state-of-the-art FSOD approaches.