DETR accomplishes end-to-end object detection through iteratively generating multiple object candidates based on image features and promoting one candidate for each ground-truth object. The traditional training procedure using one-to-one supervision in the original DETR lacks direct supervision for the object detection candidates. We aim at improving the DETR training efficiency by explicitly supervising the candidate generation procedure through mixing one-to-one supervision and one-to-many supervision. Our approach, namely MS-DETR, is simple, and places one-to-many supervision to the object queries of the primary decoder that is used for inference. In comparison to existing DETR variants with one-to-many supervision, such as Group DETR and Hybrid DETR, our approach does not need additional decoder branches or object queries. The object queries of the primary decoder in our approach directly benefit from one-to-many supervision and thus are superior in object candidate prediction. Experimental results show that our approach outperforms related DETR variants, such as DN-DETR, Hybrid DETR, and Group DETR, and the combination with related DETR variants further improves the performance.

翻译：DETR通过基于图像特征迭代生成多个目标候选框，并为每个真实目标框提升一个候选框，从而实现端到端目标检测。原始DETR中采用的一对一监督训练流程缺乏对目标检测候选框的直接监督。我们旨在通过混合监督（即结合一对一监督与一对多监督）显式指导候选框生成过程，从而提升DETR的训练效率。所提出的方法MS-DETR简洁高效，将一对多监督应用于用于推理的主解码器的目标查询中。与现有采用一对多监督的DETR变体（如Group DETR和Hybrid DETR）相比，本方法无需额外解码器分支或目标查询。主解码器的目标查询可直接从一对多监督中获益，因此在目标候选框预测方面表现更优。实验结果表明，本方法优于DN-DETR、Hybrid DETR和Group DETR等相关DETR变体，且与相关DETR变体结合后可进一步提升性能。