  In this paper, we concern on the bottom-up paradigm in multi-person pose
estimation (MPPE). Most previous bottom-up methods try to consider the relation
of instances to identify different body parts during the post processing, while
ignoring to model the relation among instances or environment in the feature
learning process. In addition, most existing works adopt the operations of
upsampling and downsampling. During the sampling process, there will be a
problem of misalignment with the source features, resulting in deviations in
the keypoint features learned by the model.
  To overcome the above limitations, we propose a convolutional neural network
for bottom-up human pose estimation. It invovles two basic modules: (i) Global
Relation Modeling (GRM) module globally learns relation (e.g., environment
context, instance interactive information) among region of image by fusing
multiple stages features in the feature learning process. It combines with the
spatial-channel attention mechanism, which focuses on achieving adaptability in
spatial and channel dimensions. (ii) Multi-branch Feature Align (MFA) module
aggregates features from multiple branches to align fused feature and obtain
refined local keypoint representation. Our model has the ability to focus on
different granularity from local to global regions, which significantly boosts
the performance of the multi-person pose estimation. Our results on the COCO
and CrowdPose datasets demonstrate that it is an efficient framework for
multi-person pose estimation.
