Methods › Computer Vision › One-Stage Object Detection Models › CornerNet

CornerNet

7 papers tagged archive 2025-07-28

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

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

PaperSourceSee Code · princeton-vl/CornerNet

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object6
Object Detection6
object-detection6
Decoder1
Keypoint Estimation1
Region Proposal1
Table Detection1
Table Recognition1
Triplet1

Usage over time archive 2025-07-28

Papers per year tagged with CornerNet: 2018 to 2022, peak 2 2 0 2018: 1 paper 2018 2019: 2 papers 2019 2020: 2 papers 2020 2021: 1 paper 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

One-Stage Object Detection ModelsObject Detection Models

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