Few-shot object detection (FSOD) often suffers from base-class bias and unstable calibration when only a few novel samples are available. We propose Prototype-Driven Alignment (PDA), a lightweight, plug-in metric head for DeFRCN that provides a prototype-based "second opinion" complementary to the linear classifier. PDA maintains support-only prototypes in a learnable identity-initialized projection space and optionally applies prototype-conditioned RoI alignment to reduce geometric mismatch. During fine-tuning, prototypes can be adapted via exponential moving average(EMA) updates on labeled foreground RoIs-without introducing class-specific parameters-and are frozen at inference to ensure strict protocol compliance. PDA employs a best-of-K matching scheme to capture intra-class multi-modality and temperature-scaled fusion to combine metric similarities with detector logits. Experiments on VOC FSOD and GFSOD benchmarks show that PDA consistently improves novel-class performance with minimal impact on base classes and negligible computational overhead.

翻译：少样本目标检测（FSOD）在仅有少量新类别样本可用时，常面临基础类别偏差和校准不稳定的问题。我们提出原型驱动对齐（PDA），这是一种轻量级、可即插即用的度量头模块，用于DeFRCN框架，提供基于原型的“第二意见”，与线性分类器形成互补。PDA在可学习的身份初始化投影空间中维护仅支持集的原型，并可选择性地应用原型条件化的感兴趣区域对齐以减少几何失配。在微调过程中，原型可通过带标签前景感兴趣区域的指数移动平均（EMA）更新进行自适应调整——无需引入类别特定参数——并在推理时冻结以确保严格遵循评估协议。PDA采用K最佳匹配方案以捕捉类内多模态特性，并通过温度缩放融合将度量相似度与检测器逻辑值相结合。在VOC FSOD和GFSOD基准测试上的实验表明，PDA能持续提升新类别性能，对基础类别影响极小，且计算开销可忽略不计。