Cu-Net: Coupled U-nets

Zhiqiang Tang, Xi Peng, Shijie Geng, Yizhe Zhu, Dimitris N. Metaxas

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations

Abstract

We design a new connectivity pattern for the U-Net shape architecture. Given several 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.

Original languageEnglish (US)
StatePublished - 2019
Event29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
Duration: Sep 3 2018Sep 6 2018

Conference

Conference29th British Machine Vision Conference, BMVC 2018
Country/TerritoryUnited Kingdom
CityNewcastle
Period9/3/189/6/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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