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CornerNet: Detecting Objects as Paired Keypoints

Authors :
Hei Law
Jia Deng
Publication Year :
2018

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.<br />Extended version with additional results. Test AP on MS COOO improved from 42.1% to 42.2% after a bug fix

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....96b2dd7178cd66fb7e46b262a6465ceb