Keypoint detection involves simultaneously detecting people and localizing their keypoints. Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. They are invariant to image rotation, shrinkage, translation, distortion, and so on.
( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
SOTA for Instance Segmentation on Cityscapes test (using extra training data)
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
The method is used to train a hand keypoint detector for single images.
We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.
#2 best model for Pose Estimation on COCO (using extra training data)
In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints.
#3 best model for Multi-Person Pose Estimation on COCO