Pose

Feature learning

Security token

Computer security

Constraint (computer-aided design)

Set (abstract data type)

Parallel computing

Computer science

Code (set theory)

FLOPS

Pattern recognition (psychology)

Mathematics

Politics

Political science

Artificial intelligence

Geometry

Representation (politics)

Programming language

Convolutional neural network

Reduction (mathematics)

Computer vision

Human pose estimation deeply relies on visual clues and anatomical constraints between parts to locate keypoints. Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints. In this paper, we propose a novel approach based on Token representation for human Pose estimation (TokenPose). In detail, each keypoint is explicitly embedded as a token to simultaneously learn constraint relationships and appearance cues from images. Extensive experiments show that the small and large TokenPose models are on par with state-of-the-art CNN-based counterparts while being more lightweight. Specifically, our TokenPose-S and TokenPose-L achieve 72.5 AP and 75.8 AP on COCO validation dataset respectively, with significant reduction in parameters (↓80.6% ; ↓ 56.8%) and GFLOPs (↓ 75.3%; ↓24.7%). Code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

TokenPose: Learning Keypoint Tokens for Human Pose Estimation