  The target of human pose estimation is to determine body part or joint
locations of each person from an image. This is a challenging problems with
wide applications. To address this issue, this paper proposes an augmented
parallel-pyramid net with attention partial module and differentiable auto-data
augmentation. Technically, a parallel pyramid structure is proposed to
compensate the loss of information. We take the design of parallel structure
for reverse compensation. Meanwhile, the overall computational complexity does
not increase. We further define an Attention Partial Module (APM) operator to
extract weighted features from different scale feature maps generated by the
parallel pyramid structure. Compared with refining through upsampling operator,
APM can better capture the relationship between channels. At last, we proposed
a differentiable auto data augmentation method to further improve estimation
accuracy. We define a new pose search space where the sequences of data
augmentations are formulated as a trainable and operational CNN component.
Experiments corroborate the effectiveness of our proposed method. Notably, our
method achieves the top-1 accuracy on the challenging COCO keypoint benchmark
and the state-of-the-art results on the MPII datasets.
