Methods > Computer Vision > Object Detection Models

CornerNet

Introduced by Law et al. in CornerNet: Detecting Objects as Paired Keypoints

CornerNet is an object detection model that detects 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. It also utilises corner pooling, a new type of pooling layer than helps the network better localize corners.

Source: CornerNet: Detecting Objects as Paired Keypoints

Latest Papers

PAPER DATE
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
Cheng ChiFangyun WeiHan Hu
2020-10-29
MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection
| Abdullah RashwanRishav AgarwalAgastya KalraPascal Poupart
2020-01-09
CenterNet: Keypoint Triplets for Object Detection
| Kaiwen DuanSong BaiLingxi XieHonggang QiQingming HuangQi Tian
2019-04-17
Bottom-up Object Detection by Grouping Extreme and Center Points
| Xingyi ZhouJiacheng ZhuoPhilipp Krähenbühl
2019-01-23
CornerNet: Detecting Objects as Paired Keypoints
| Hei LawJia Deng
2018-08-03

Tasks

TASK PAPERS SHARE
Object Detection 5 100.00%

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