We present in this paper a novel query formulation using dynamic anchor boxes
for DETR (DEtection TRansformer) and offer a deeper understanding of the role
of queries in DETR. This new formulation directly uses box coordinates as
queries in Transformer decoders and dynamically updates them layer-by-layer.
Using box coordinates not only helps using explicit positional priors to
improve the query-to-feature similarity and eliminate the slow training
convergence issue in DETR, but also allows us to modulate the positional
attention map using the box width and height information. Such a design makes
it clear that queries in DETR can be implemented as performing soft ROI pooling
layer-by-layer in a cascade manner. As a result, it leads to the best
performance on MS-COCO benchmark among the DETR-like detection models under the
same setting, e.g., AP 45.7\% using ResNet50-DC5 as backbone trained in 50
epochs. We also conducted extensive experiments to confirm our analysis and
verify the effectiveness of our methods. Code is available at
https://github.com/SlongLiu/DAB-DETR.

本文提出了一种使用动态锚框的新型查询公式，用于 DETR，并对查询在 DETR 中的作用提供了更深入的理解。通过在 Transformer 解码器中直接使用盒子坐标作为查询，动态地逐层更新它们，这种新公式不仅通过使用显式位置先验来提高查询与特征相似度，消除 DETR 中的慢训练收敛问题，而且还允许我们使用盒子宽度和高度信息调制位置注意力映射。