  Existing anchor-based and anchor-free object detectors in multi-stage or
one-stage pipelines have achieved very promising detection performance.
However, they still encounter the design difficulty in hand-crafted 2D anchor
definition and the learning complexity in 1D direct location regression. To
tackle these issues, in this paper, we propose a novel detector coined as
ScopeNet, which models anchors of each location as a mutually dependent
relationship. This approach quantizes the prediction space and employs a
coarse-to-fine strategy for localization. It achieves superior flexibility as
in the regression based anchor-free methods, while produces more precise
prediction. Besides, an inherit anchor selection score is learned to indicate
the localization quality of the detection result, and we propose to better
represent the confidence of a detection box by combining the
category-classification score and the anchor-selection score. With our concise
and effective design, the proposed ScopeNet achieves state-of-the-art results
on COCO
