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Detection transformer (DETR) relies on one-to-one assignment, assigning oneground-truth object to one prediction, for end-to-end detection without NMSpost-processing. It is known that one-to-many assignment, assigning oneground-truth object to multiple predictions, succeeds in detection methods suchas Faster R-CNN and FCOS. While the naive one-to-many assignment does not workfor DETR, and it remains challenging to apply one-to-many assignment for DETRtraining. In this paper, we introduce Group DETR, a simple yet efficient DETRtraining approach that introduces a group-wise way for one-to-many assignment.This approach involves using multiple groups of object queries, conductingone-to-one assignment within each group, and performing decoder self-attentionseparately. It resembles data augmentation with automatically-learned objectquery augmentation. It is also equivalent to simultaneously trainingparameter-sharing networks of the same architecture, introducing moresupervision and thus improving DETR training. The inference process is the sameas DETR trained normally and only needs one group of queries without anyarchitecture modification. Group DETR is versatile and is applicable to variousDETR variants. The experiments show that Group DETR significantly speeds up thetraining convergence and improves the performance of various DETR-based models.Code will be available at \url{https://github.com/Atten4Vis/GroupDETR}.

Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment