OCPAN: Multi-Person Pose Estimation with More Attention on Residual Information

Redundant information generated in the process of feature fusion and residual information always remain challenging in multi-person pose estimation. In this Letter, the authors present an optimised cascaded pyramid attention network composed of two novel modules to reduce the redundant information and highlight the residual information for more accurate results. The first module, called channel optimisation module (COM), optimises the channel at different levels. The proposed COM consisting of convolution layers and two pooling layers performs on the feature maps Conv-2 ∼ 5 of different resolution to reduce the redundant information after convolution operation. The second module, called residual attention bottleneck module (RABM), highlights the residual information in the bottleneck. Through adopting the attention mechanism in the bottleneck, RABM performs a significant role in reducing the redundant information. In contrast to the previous work, they attach more attention to residual information, helping to tackle the challenging problem for the ‘hard’ keypoints. By merging COM and RABM into Cascaded Pyramid Network, the authors' method achieves competitive results of 74.6 AP on the COCO keypoint benchmark, outperforming the state-of-the-art results.