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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Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them.
By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.
Interestingly, we found that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before.
SOTA for Pose Estimation on COCO (using extra training data)
Neural Architecture Search (NAS) technologies have been successfully performed for efficient neural architectures for tasks such as image classification and semantic segmentation.
#6 best model for Keypoint Detection on COCO
We introduce a novel CNN-based feature point detector - GLAMpoints - learned in a semi-supervised manner.