Box-DETR: Understanding and Boxing Conditional Spatial Queries
Wenze Liu Hao Lu Yuliang Liu Zhiguo Cao
Conditional spatial queries are recently introduced into DEtection
TRansformer (DETR) to accelerate convergence. In DAB-DETR, such queries are
modulated by the so-called conditional linear projection at each decoder stage,
aiming to search for positions of interest such as the four extremities of the
box. Each decoder stage progressively updates the box by predicting the anchor
box offsets, while in cross-attention only the box center is informed as the
reference point. The use of only box center, however, leaves the width and
height of the previous box unknown to the current stage, which hinders accurate
prediction of offsets. We argue that the explicit use of the entire box
information in cross-attention matters. In this work, we propose Box Agent to
condense the box into head-specific agent points. By replacing the box center
with the agent point as the reference point in each head, the conditional
cross-attention can search for positions from a more reasonable starting point
by considering the full scope of the previous box, rather than always from the
previous box center. This significantly reduces the burden of the conditional
linear projection. Experimental results show that the box agent leads to not
only faster convergence but also improved detection performance, e.g., our
single-scale model achieves $44.2$ AP with ResNet-50 based on DAB-DETR. Our Box
Agent requires minor modifications to the code and has negligible computational
workload. Code is available at https://github.com/tiny-smart/box-detr.