To refer to this page use:
|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 42.2% AP on MS COCO, outperforming all existing one-stage detectors.|
|Citation:||Law, Hei, and Jia Deng. "CornerNet: Detecting Objects as Paired Keypoints." International Journal of Computer Vision 128, no. 3 (2020): pp. 642-656. doi:10.1007/s11263-019-01204-1|
|Pages:||642 - 656|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||International Journal of Computer Vision|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.