Multistage attention network for human pose estimation

Human pose estimation is a fundamental yet challenging task in computer vision. Although many methods have achieved significant improvement, they are still insufficient for the fusion of feature maps at different stages, such as the stacked hourglass network (SHNet). The SHNet is a classic human pose estimation network that extracts multiscale features through stacked multistage downsampling and upsampling operations. We propose a multistage attention mechanism to fuse the multistage feature maps. Furthermore, we apply it in the SHNet to propose a multistage attention network (MANet). In the experiments, we demonstrated the effectiveness of MANet in human pose estimation on the common objects in context dataset and the MPII human pose dataset.