  Movement and pose assessment of newborns lets experienced pediatricians
predict neurodevelopmental disorders, allowing early intervention for related
diseases. However, most of the newest AI approaches for human pose estimation
methods focus on adults, lacking publicly benchmark for infant pose estimation.
In this paper, we fill this gap by proposing infant pose dataset and Deep
Aggregation Vision Transformer for human pose estimation, which introduces a
fast trained full transformer framework without using convolution operations to
extract features in the early stages. It generalizes Transformer + MLP to
high-resolution deep layer aggregation within feature maps, thus enabling
information fusion between different vision levels. We pre-train AggPose on
COCO pose dataset and apply it on our newly released large-scale infant pose
estimation dataset. The results show that AggPose could effectively learn the
multi-scale features among different resolutions and significantly improve the
performance of infant pose estimation. We show that AggPose outperforms hybrid
model HRFormer and TokenPose in the infant pose estimation dataset. Moreover,
our AggPose outperforms HRFormer by 0.8 AP on COCO val pose estimation on
average. Our code is available at github.com/SZAR-LAB/AggPose.
