关键词: 卷积神经网络, 人体姿态估计, 多分辨率特征表征融合, 空间域注意力机制, 通道域注意力机制

Abstract:

In order to solve the problem that spatial information of feature maps is unable to effectively utilize when multi-resolution feature representations are directly fused in human pose estimation task, the multi-resolution human pose estimation network is proposed based on the High-Resolution Net（HRNet） for structural design, namely GCT-Nonlocal Net （GNNet）, which combines both channel domain and spatial domain attention mechanism and contains improved exchange units, Gateneck module and Gateblock module. The exchange units are improved to extract more useful spatial information from the various feature representations by adding spatial attention mechanism before the multi-scale fusions, which make the information fusions between the different resolution representations better and result in the final high-resolution representation containing richer representation information. In addition, the Gateneck module and the Gateblock module are able to model channel relationships more explicitly to extract channel information more effectively by introducing channel attention mechanism. The verification results on MS COCO VAL 2017 dataset show that the proposed GNNet achieves higher accuracy with the similar parameter and computation complexities, compared with the state-of-the-art human pose estimation network, HRNet, and the mAP is improved by 1.4 percentage points. As a result, the improved exchanged units make multi-scale information fusions more effective between the various resolution representations.

Key words: convolutional neural network, human pose estimation, multi-resolution feature representation fusion, spatial attention mechanism, channel attention mechanism