  Region anchors are the cornerstone of modern object detection techniques.
State-of-the-art detectors mostly rely on a dense anchoring scheme, where
anchors are sampled uniformly over the spatial domain with a predefined set of
scales and aspect ratios. In this paper, we revisit this foundational stage.
Our study shows that it can be done much more effectively and efficiently.
Specifically, we present an alternative scheme, named Guided Anchoring, which
leverages semantic features to guide the anchoring. The proposed method jointly
predicts the locations where the center of objects of interest are likely to
exist as well as the scales and aspect ratios at different locations. On top of
predicted anchor shapes, we mitigate the feature inconsistency with a feature
adaption module. We also study the use of high-quality proposals to improve
detection performance. The anchoring scheme can be seamlessly integrated into
proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher
recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt
Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively
improving the detection mAP by 2.2%, 2.7% and 1.2%. Code will be available at
https://github.com/open-mmlab/mmdetection.
