computer-vision-7
Keypoint Detection
3260 papers • 126 benchmarks • 313 datasets
Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality. ( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in keypoint-detection-7
Trend
Dataset
Best Model
Actions
MS COCO
MS COCO
COCO test-dev
COCO test-dev
Libraries
iUse these libraries to find keypoint-detection-7 models and implementations
12 papers 5,927
Datasets
Subtasks
No subtasks available.
Most implemented papers
Mask R-CNN
Kaiming He, Georgia Gkioxari, Ross B. Girshick, Piotr Dollár•Sun Mar 19 2017
This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
30711
