  We design a new connectivity pattern for the U-Net architecture. Given
several stacked U-Nets, we couple each U-Net pair through the connections of
their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling
connections could make the information flow more efficiently across U-Nets. The
feature reuse across U-Nets makes each U-Net very parameter efficient. We
evaluate the coupled U-Nets on two benchmark datasets of human pose estimation.
Both the accuracy and model parameter number are compared. The CU-Net obtains
comparable accuracy as state-of-the-art methods. However, it only has at least
60% fewer parameters than other approaches.
