  Common object detection models consist of classification and regression
branches, due to different task drivers, these two branches have different
sensibility to the features from the same scale level and the same spatial
location. The point-based prediction method, which is based on the assumption
that the high classification confidence point has the high regression quality,
leads to the misalignment problem. Our analysis shows, the problem is further
composed of scale misalignment and spatial misalignment specifically. We aim to
resolve the phenomenon at minimal cost: a minor adjustment of the head network
and a new label assignment method replacing the rigid one. Our experiments show
that, compared to the baseline FCOS, a one-stage and anchor-free object
detection model, our model consistently get around 3 AP improvement with
different backbones, demonstrating both simplicity and efficiency of our
method.
