DRSI-Net: Dual-Residual Spatial Interaction Network for Multi-Person
  Pose Estimation
Shang Wu Bin Wang
Multi-person pose estimation (MPPE), which aims to locate keypoints for all
persons in the frames, is an active research branch of computer vision.
Variable human poses and complex scenes make MPPE dependent on both local
details and global structures, and the absence of them may cause keypoint
feature misalignment. In this case, high-order spatial interactions that can
effectively link the local and global information of features are particularly
important. However, most methods do not have spatial interactions, and a few
methods have low-order spatial interactions but they are difficult to achieve a
good balance between accuracy and complexity. To address the above problems, a
Dual-Residual Spatial Interaction Network (DRSI-Net) for MPPE with high
accuracy and low complexity is proposed in this paper. DRSI-Net recursively
performs residual spatial information interactions on neighbor features, so
that more useful spatial information can be retained and more similarities can
be obtained between shallow and deep extracted features. The channel and
spatial dual attention mechanism introduced in the multi-scale feature fusion
also helps the network to adaptively focus on features relevant to target
keypoints and further refine generated poses. At the same time, by optimizing
interactive channel dimensions and dividing gradient flow, the spatial
interaction module is designed to be lightweight, which reduces the complexity
of the network. According to the experimental results on the COCO dataset, the
proposed DRSI-Net outperforms other state-of-the-art methods in both accuracy
and complexity.