  Keypoint-based detectors have achieved pretty-well performance. However,
incorrect keypoint matching is still widespread and greatly affects the
performance of the detector. In this paper, we propose CentripetalNet which
uses centripetal shift to pair corner keypoints from the same instance.
CentripetalNet predicts the position and the centripetal shift of the corner
points and matches corners whose shifted results are aligned. Combining
position information, our approach matches corner points more accurately than
the conventional embedding approaches do. Corner pooling extracts information
inside the bounding boxes onto the border. To make this information more aware
at the corners, we design a cross-star deformable convolution network to
conduct feature adaption. Furthermore, we explore instance segmentation on
anchor-free detectors by equipping our CentripetalNet with a mask prediction
module. On MS-COCO test-dev, our CentripetalNet not only outperforms all
existing anchor-free detectors with an AP of 48.0% but also achieves comparable
performance to the state-of-the-art instance segmentation approaches with a
40.2% MaskAP. Code will be available at
https://github.com/KiveeDong/CentripetalNet.
