Calibrate Before Adapt: Training-Free Pseudo-Label Calibration for Semi-Supervised Cross-Domain Few-Shot Detection

Semi-supervised cross-domain few-shot object detection (SemiCDFSOD) is a practical yet underexplored task that requires adapting detectors to novel domains using scarce support examples alongside abundant unlabeled data. While pseudo-labeling via open-set detectors offers a direct pathway to exploit unlabeled data, it is fundamentally impaired by two intrinsic defects: semantic misclassification, where localized objects are assigned incorrect categories or suppressed as background, and low recall, where the majority of objects are missed entirely. To systematically address these limitations, we propose a training-free pseudo-label calibration framework for Semi-CDFSOD. First, we construct robust class prototypes from the scarce labeled supports and high-confidence pseudo-labels to rectify semantic misclassifications, dynamically recalibrating noisy category assignments while preserving welllocalized detections. Second, we introduce a visual self-prompting mechanism that harnesses high-confidence pseudo-labels as prompts for the Segment Anything Model (SAM), generating supplementary proposals to recover undetected objects and dramatically enhance recall. The harmonized pseudo-labels, together with the support samples, are then employed to adapt the final detector. We establish the first comprehensive benchmark for Semi-CDFSOD, spanning six diverse domains. Extensive experiments demonstrate that our method consistently outperforms existing approaches. The code will be released at https://github.com/ xSheep123/Semi-CDFSOD.git.