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CornerNet: Detecting Objects as Paired Keypoints
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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.
Forward citations
Cited by 2 Pith papers
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Shape-Aware Oriented Bounding Box (OBB) to Horizontal Bounding Box (HBB) Conversion
A shape-aware OBB-to-HBB conversion using a fitted superellipse hull model is proposed, but the derived projection equations are internally inconsistent and the empirical claims are partly overstated.
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SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning
The submitted body is an unrelated survey, not the SHeRL-FL method claimed in the metadata.
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