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}.

翻译：近期提出的DETR变体因其简洁的流程和卓越的性能，在多种场景下取得了巨大进展。然而，学习得到的查询通常通过探索全局上下文来生成最终集合预测，导致冗余负担和不准确的结果。具体而言，一个查询通常需要负责不同尺度和位置的物体，这对查询本身而言是一项挑战，并且会在查询之间引发空间资源竞争。为缓解这一问题，我们提出Team DETR，通过利用查询协作和位置约束来更精确地捕捉感兴趣的目标。我们还动态适应每个查询成员的预测偏好，为查询提供更好的尺度和空间先验信息。此外，所提出的Team DETR足够灵活，可适配其他现有DETR变体，且不增加参数和计算量。在COCO数据集上的大量实验表明，Team DETR取得了显著增益，尤其是针对小物体和大物体。代码开源于\url{https://github.com/horrible-dong/TeamDETR}。