Abstract: In recent years,both convolutional neural network and Transformer have made progress in the field of human pose estimation.Convolutional neural network(CNN) is good at extracting local features,and Transformer does well in capturing global representations.However,there are few studies on the combination of the two to achieve human pose estimation,as the same time the results are not good.Aiming at solving this problem,this paper proposes a model CNPose(CNN-Nest Pose) that couples local features and global representations.The local-global feature coupling module of this framework uses multi-head attention calculation method and residual structure to deeply couple local features and global representations.At the same time this paper proposes a local-global information exchange module to solve the problem that therange of data sources of local features and global representationis inconsistent in the local-global feature coupling module during the calculation process.The CNPose framework has been verified on COCO-val2017 and COCO-dev-test2017 datasets.Experiment results show that the CNPose model using the coupling of local features and global representations has superior performance compared to similar methods.

Key words: Human pose estimation, Transformer, Convolutional neural networks, Local features, Global representations, Feature coupling, Attention