Abstract. We propose CornerNet, a new approach to object detection
where we detect an object bounding box as a pair of keypoints, the
top-left corner and the bottom-right corner, using a single convolution
neural network. By detecting objects as paired keypoints, we eliminate
the need for designing a set of anchor boxes commonly used in prior
single-stage detectors. In addition to our novel formulation, we introduce
corner pooling, a new type of pooling layer that helps the network better
localize corners. Experiments show that CornerNet achieves a 42.1% AP
on MS COCO, outperforming all existing one-stage detectors