顶刊TGRS 2026！武大提出SKYDET：将DINOv3迁移到遥感小目标检测

TL;DR - SKYDET (Wuhan University et al., IEEE TGRS 2026) is an end-to-end Transformer detector that transfers the DINOv3 vision foundation model to small-object detection in remote sensing imagery, with code and results on DOTA-v1.0, AI-TOD, and NWPU VHR-10.

• Keeps the DINOv3 backbone frozen and trains only adapters, encoder, and detection head, to preserve general pretrained representations and limit catastrophic forgetting/overfitting on small remote-sensing datasets.
• A Semantic-Guided Adapter (SGA) converts ViT single-scale features into multi-scale ones by fusing a spatial-prior branch (edges/texture) with attention maps generated from deep semantic layers acting as background-suppressing filters.
• A Cross-Fusion Encoder (CFE) with a Reciprocal Guidance Module (RGM) applies bidirectional guidance — deep features give spatial attention to shallow ones, shallow features give channel attention to deep ones — to fix cross-scale misalignment and semantic ambiguity.
• Two configurations: SKYDET-C (ConvNeXt backbone, favors recall/efficiency/deployment) and SKYDET-T (ViT backbone + SGA, stronger on strict localization metrics); ablations report that freezing DINOv3 plus SGA, CFE, and RGM each help.