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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Keypoint detection usually results in a large number of keypoints which are mostly clustered, redundant, and noisy.
We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture.
This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images.
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
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.
We introduce a novel CNN-based feature point detector - GLAMpoints - learned in a semi-supervised manner.
Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets.