  Recent proposed DETR variants have made tremendous progress in various
scenarios due to their streamlined processes and remarkable performance.
However, the learned queries usually explore the global context to generate the
final set prediction, resulting in redundant burdens and unfaithful results.
More specifically, a query is commonly responsible for objects of different
scales and positions, which is a challenge for the query itself, and will cause
spatial resource competition among queries. To alleviate this issue, we propose
Team DETR, which leverages query collaboration and position constraints to
embrace objects of interest more precisely. We also dynamically cater to each
query member's prediction preference, offering the query better scale and
spatial priors. In addition, the proposed Team DETR is flexible enough to be
adapted to other existing DETR variants without increasing parameters and
calculations. Extensive experiments on the COCO dataset show that Team DETR
achieves remarkable gains, especially for small and large objects. Code is
available at \url{https://github.com/horrible-dong/TeamDETR}.
