Open-set object detectors, as exemplified by Grounding DINO, have attracted significant attention due to their remarkable performance on in-domain datasets like Common Objects in Context (COCO) after only few-shot fine-tuning. However, their generalization capabilities in cross-domain scenarios remain substantially inferior to their in-domain few-shot performance. Prior work on fine-tuning Grounding DINO for cross-domain few-shot object detection has primarily focused on data augmentation, leaving broader systemic optimizations unexplored. To bridge this gap, we propose a comprehensive end-to-end fine-tuning framework specifically designed to optimize Grounding DINO for cross-domain few-shot scenarios. In addition, we propose Mixture-of-Experts (MoE)-Grounding DINO, a novel architecture that integrates the MoE architecture to enhance adaptability in cross-domain settings. Our approach demonstrates a significant 15.4 Mean Average Precision (mAP) improvement over the Grounding DINO baseline on the Roboflow20-VL benchmark, establishing a new state of the art for cross-domain few-shot object detection (CD-FSOD). The source code and models will be made available upon publication.
