  Label assignment plays a significant role in modern object detection models.
Detection models may yield totally different performances with different label
assignment strategies. For anchor-based detection models, the IoU (Intersection
over Union) threshold between the anchors and their corresponding ground truth
bounding boxes is the key element since the positive samples and negative
samples are divided by the IoU threshold. Early object detectors simply utilize
the fixed threshold for all training samples, while recent detection algorithms
focus on adaptive thresholds based on the distribution of the IoUs to the
ground truth boxes. In this paper, we introduce a simple while effective
approach to perform label assignment dynamically based on the training status
with predictions. By introducing the predictions in label assignment, more
high-quality samples with higher IoUs to the ground truth objects are selected
as the positive samples, which could reduce the discrepancy between the
classification scores and the IoU scores, and generate more high-quality
boundary boxes. Our approach shows improvements in the performance of the
detection models with the adaptive label assignment algorithm and lower
bounding box losses for those positive samples, indicating more samples with
higher-quality predicted boxes are selected as positives.
