Meta-DETR performs image-level few-shot object detection by directly predicting bounding boxes without region proposal networks, bypassing the proposal quality gap that limits R-CNN-based detectors. It exploits inter-class correlation to improve generalization from base to novel classes. The model uses meta-learning on object queries rather than region proposals to achieve state-of-the-art few-shot detection performance.