Knowledge Lies in Layers: Internalizing Foundation Visual Priors for Few-Shot Remote Sensing Object Detection

Few-shot remote sensing object detection is limited by scarce annotations, hindering robust and generalizable representation learning. Existing methods increasingly leverage vision foundation models to enrich visual representations, but often rely on fixed-depth features or unified multi-layer integration, overlooking depth-dependent representation characteristics. Through a systematic layer-wise analysis of a vision foundation model, we find clear depth-dependent differences: low-to-mid layers exhibit stronger variation across spatial locations, while mid-to-high layers show stronger alignment between patch responses and object regions. Such a phenomenon implies that different network depths offer distinct advantages in spatial representation and object-aware semantics, and that exploiting these advantages may provide a lightweight solution to improve few-shot detection under limited supervision. Accordingly, we propose LayerFi-FSOD, a hierarchical foundation visual prior internalization framework that exploits foundation-model knowledge according to its depth-dependent representation characteristics. Specifically, the Adaptive Dense Prior Injection Module aggregates low-to-mid-layer dense priors to enhance multi-scale detection features, while the Dual-Grained Structural-Relational Guidance Module guides the student detector to absorb knowledge from different depths through local spatial structures and global semantic relations. Extensive experiments on the DIOR and NWPU VHR-10 datasets validate the effectiveness of LayerFi-FSOD. Notably, the vision foundation model is used only as a visual prior provider during training and is completely removed at inference, introducing no additional foundation-model inference overhead. The relevant code will be made publicly available upon acceptance.