  The complex nature of combining localization and classification in object
detection has resulted in the flourished development of methods. Previous works
tried to improve the performance in various object detection heads but failed
to present a unified view. In this paper, we present a novel dynamic head
framework to unify object detection heads with attentions. By coherently
combining multiple self-attention mechanisms between feature levels for
scale-awareness, among spatial locations for spatial-awareness, and within
output channels for task-awareness, the proposed approach significantly
improves the representation ability of object detection heads without any
computational overhead. Further experiments demonstrate that the effectiveness
and efficiency of the proposed dynamic head on the COCO benchmark. With a
standard ResNeXt-101-DCN backbone, we largely improve the performance over
popular object detectors and achieve a new state-of-the-art at 54.0 AP.
Furthermore, with latest transformer backbone and extra data, we can push
current best COCO result to a new record at 60.6 AP. The code will be released
at https://github.com/microsoft/DynamicHead.

Computer Vision and Pattern Recognition