A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps

Dimensions overall score 7.0

• The proposed hybrid ensemble decoder improves generalization in few-shot object detection without introducing additional parameters.method 90%
• The method achieves an average performance of 41.9 on the RF100-VL benchmark in the 10-shot setting, significantly outperforming SAM3 which scores 35.7.result 95%
• The hybrid ensemble decoder produces fewer overconfident predictions on out-of-distribution (OOD) data, indicating improved robustness under domain shifts.result 85%
• The proposed unified progressive fine-tuning framework with a plateau-aware learning rate schedule stabilizes optimization for few-shot adaptation.method 88%
• The method establishes a strong baseline in few-shot object detection, demonstrating adaptability and effectiveness across heterogeneous downstream tasks.result 80%
• The decoder design encourages prediction diversity by employing denoising queries either inherited from a shared layer or newly initialized across parallel branches.technical 90%
• Effectively adapting pretrained detectors under significant domain shifts remains an active research problem, which this work addresses.limitation 75%

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