  Anchor-free detectors basically formulate object detection as dense
classification and regression. For popular anchor-free detectors, it is common
to introduce an individual prediction branch to estimate the quality of
localization. The following inconsistencies are observed when we delve into the
practices of classification and quality estimation. Firstly, for some adjacent
samples which are assigned completely different labels, the trained model would
produce similar classification scores. This violates the training objective and
leads to performance degradation. Secondly, it is found that detected bounding
boxes with higher confidences contrarily have smaller overlaps with the
corresponding ground-truth. Accurately localized bounding boxes would be
suppressed by less accurate ones in the Non-Maximum Suppression (NMS)
procedure. To address the inconsistency problems, the Dynamic Smooth Label
Assignment (DSLA) method is proposed. Based on the concept of centerness
originally developed in FCOS, a smooth assignment strategy is proposed. The
label is smoothed to a continuous value in [0, 1] to make a steady transition
between positive and negative samples. Intersection-of-Union (IoU) is predicted
dynamically during training and is coupled with the smoothed label. The dynamic
smooth label is assigned to supervise the classification branch. Under such
supervision, quality estimation branch is naturally merged into the
classification branch, which simplifies the architecture of anchor-free
detector. Comprehensive experiments are conducted on the MS COCO benchmark. It
is demonstrated that, DSLA can significantly boost the detection accuracy by
alleviating the above inconsistencies for anchor-free detectors. Our codes are
released at https://github.com/YonghaoHe/DSLA.
