Stabilizing Few-Shot Object Detection with Language-Conditioned Probabilistic Prototypes

Few-shot object detection (FSOD) requires detectors to adapt to novel categories from only a few labeled instances, where prototype-based transfer methods have become a strong and efficient paradigm. However, we observe that such methods remain unstable in extreme low-shot regimes. We attribute this instability to two coupled factors: semantically unanchored query initialization, which yields high-variance cold-start optimization, and deterministic prototype matching, which over-trusts noisy or occluded support regions. To address these limitations, we propose Semantic Fine-Grained Prototype Distillation (SFPD), a language-conditioned probabilistic adaptation framework for prototype-based FSOD. SFPD introduces language-conditioned feature queries to provide semantic anchors for novel-class adaptation, uncertainty-aware prototype distillation to down-weight unreliable support evidence through heteroscedastic Gaussian modeling, and component-guided part-aware prototypes to refine fine-grained semantic-visual alignment. These modules act during adaptation and preserve the original detector path at inference, introducing no additional inference FLOPs. Experiments on PASCAL VOC and MS-COCO show that SFPD consistently improves a strong FPD baseline, with especially clear gains in the most challenging low-shot settings, e.g., a 3.9-point nAP50 improvement on VOC Split 2 under 1-shot. Further ablations, multi-seed evaluation, and convergence analysis indicate that SFPD improves both accuracy and adaptation stability.