Papers › CU-Net: Coupled U-Nets

CU-Net: Coupled U-Nets

20 Aug 2018arXiv:1808.06521archive 2025-07-28

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

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.

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Code

zhiqiangdon/CU-Net mentioned on GitHubpytorch report

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Tasks

Pose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Pose Estimation MPII Human Pose CU-Net PCKh-0.5 89.4 #30 of 46 Archive leaderboard report

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Methods

Concatenated Skip ConnectionConvolutionMax PoolingReLUU-Net

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