  A standard one-stage detector is comprised of two tasks: classification and
regression. Anchors of different shapes are introduced for each location in the
feature map to mitigate the challenge of regression for multi-scale objects.
However, the performance of classification can degrade due to the highly
class-imbalanced problem in anchors. Recently, many anchor-free algorithms have
been proposed to classify locations directly. The anchor-free strategy benefits
the classification task but can lead to sup-optimum for the regression task due
to the lack of prior bounding boxes. In this work, we propose a semi-anchored
framework. Concretely, we identify positive locations in classification, and
associate multiple anchors to the positive locations in regression. With
ResNet-101 as the backbone, the proposed semi-anchored detector achieves 43.6%
mAP on COCO data set, which demonstrates the state-of-art performance among
one-stage detectors.
