CornerNet: Detecting Objects as Paired Keypoints
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 the corners. Experiments show that CornerNet achieves a 42.1% AP on MS COCO, outperforming all existing one-stage detectors.
Cite
Text
Law and Deng. "CornerNet: Detecting Objects as Paired Keypoints." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01264-9_45Markdown
[Law and Deng. "CornerNet: Detecting Objects as Paired Keypoints." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/law2018eccv-cornernet/) doi:10.1007/978-3-030-01264-9_45BibTeX
@inproceedings{law2018eccv-cornernet,
title = {{CornerNet: Detecting Objects as Paired Keypoints}},
author = {Law, Hei and Deng, Jia},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01264-9_45},
url = {https://mlanthology.org/eccv/2018/law2018eccv-cornernet/}
}