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.
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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 (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.
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
#4 best model for Instance Segmentation on COCO
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
We present an approach to efficiently detect the 2D pose of multiple people in an image.
#4 best model for Multi-Person Pose Estimation on MPII Multi-Person
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.
SOTA for Multi-Person Pose Estimation on COCO
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
#3 best model for Pose Estimation on COCO
In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints.
#2 best model for Multi-Person Pose Estimation on COCO