CornerNet : Detecting Objects as Paired Keypoints

CornerNet is a different object detection technique where we detects the objects bounding box by a paired key-points, the top-left corner and the bottom-right corner using a single convolution neural network. By detecting the key points, it eliminates the need of different anchor boxes commonly used in single stage detectors. In this paper by Hei Law and Jia Deng from Princeton University, they have introduced a new approach to object detection which outperforms all the single stage detectors. CornetNet introduces a new type of pooling layer called Corner Pooling, that helps localizing the corners. The Net achieves 42.2% AP on MS COCO dataset.