Transcription of CornerNet: Detecting Objects as Paired Keypoints
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CornerNet: Detecting Objects as Paired Keypoints
CornerNet: Detecting Objects asPaired KeypointsHei Law[0000 0003 1009 164X], Jia Deng[0000 0001 9594 4554]University of Michigan, Ann propose CornerNet, a new approach to object detectionwhere we detect an object bounding box as a pair of Keypoints , thetop-left corner and the bottom-right corner, using a single convolutionneural network. By Detecting Objects as Paired Keypoints , we eliminatethe need for designing a set of anchor boxes commonly used in priorsingle-stage detectors. In addition to our novel formulation,we introducecorner pooling, a new type of pooling layer that helps the network betterlocalize corners. Experiments show that CornerNet achieves a APon MS COCO, outperforming all existing one-stage : object Detection1 IntroductionObject detectors based on convolutional neural networks (ConvNets) [20, 36,15]have achieved state-of-the-art results on various challenging benchmarks [24, 8,9].
1 Introduction Object detectors based on convolutional neural networks (ConvNets) [20,36,15] have achieved state-of-the-art results on various challenging benchmarks [24,8, 9]. A common component of state-of-the-art approaches is anchor boxes [32, 25], which are boxes of various sizes and aspect ratios that serve as detection candidates.
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