@inproceedings{f92113f54ef94512821bb65b7ec3a832,
title = "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 corners. Experiments show that CornerNet achieves a 42.1% AP on MS COCO, outperforming all existing one-stage detectors.",
keywords = "Object detection",
author = "Hei Law and Jia Deng",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01264-9_45",
language = "English (US)",
isbn = "9783030012632",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "765--781",
editor = "Vittorio Ferrari and Cristian Sminchisescu and Yair Weiss and Martial Hebert",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
address = "Germany",
}