  To achieve more accurate 2D human pose estimation, we extend the successful
encoder-decoder network, simple baseline network (SBN), in three ways. To
reduce the quantization errors caused by the large output stride size, two more
decoder modules are appended to the end of the simple baseline network to get
full output resolution. Then, the global context blocks (GCBs) are added to the
encoder and decoder modules to enhance them with global context features.
Furthermore, we propose a novel spatial-attention-based multi-scale feature
collection and distribution module (SA-MFCD) to fuse and distribute multi-scale
features to boost the pose estimation. Experimental results on the MS COCO
dataset indicate that our network can remarkably improve the accuracy of human
pose estimation over SBN, our network using ResNet34 as the backbone network
can even achieve the same accuracy as SBN with ResNet152, and our networks can
achieve superior results with big backbone networks.
