  Label assignment (LA), which aims to assign each training sample a positive
(pos) and a negative (neg) loss weight, plays an important role in object
detection. Existing LA methods mostly focus on the design of pos weighting
function, while the neg weight is directly derived from the pos weight. Such a
mechanism limits the learning capacity of detectors. In this paper, we explore
a new weighting paradigm, termed dual weighting (DW), to specify pos and neg
weights separately. We first identify the key influential factors of pos/neg
weights by analyzing the evaluation metrics in object detection, and then
design the pos and neg weighting functions based on them. Specifically, the pos
weight of a sample is determined by the consistency degree between its
classification and localization scores, while the neg weight is decomposed into
two terms: the probability that it is a neg sample and its importance
conditioned on being a neg sample. Such a weighting strategy offers greater
flexibility to distinguish between important and less important samples,
resulting in a more effective object detector. Equipped with the proposed DW
method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1x
schedule, outperforming other existing LA methods. It consistently improves the
baselines on COCO by a large margin under various backbones without bells and
whistles. Code is available at https://github.com/strongwolf/DW.
