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