Multi scale Attention Aided Multi Resolution Network for Human Pose Estimation
In this paper, we propose attention maps at various scales on multi resolution feature extractor baseline network for human pose estimation. The baseline network captures information across various scales with the help of repeated bottom up and top down approach using successive pooling and up sampling. We propose a network named Refinement Net for regressing the predicted heatmaps to 2D joint locations to remove ambiguities in predicted position. We experiment with three levels of attention schemes global, heatmap and multi resolution. Attention masks helps in generating basin of attraction that helps the network on deciding where to "look" The proposed network performance is at par with the state of the art two dimensional pose estimation methods on MPII dataset.
PReMI