Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object.
( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )
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To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator.
Ranked #5 on 3D Human Pose Estimation on MPI-INF-3DHP
We show that LearningLoss++ outperforms in identifying scenarios where the model is likely to perform poorly, which on model refinement translates into reliable performance in the open world.
This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method.
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
Ranked #11 on Pose Estimation on COCO test-dev
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks.
Ranked #14 on Object Detection on COCO test-dev
Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions.
Existing approaches for multi-view multi-person 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views and solve for the 3D pose estimation for each person.