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U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

In this paper, we design a simple yet powerful deep network architecture, U2-Net, for salient object detection (SOD). The architecture of our U2-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U2-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U2-Net† (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net.

Citation

X. Qin, Z. Zhang, C. Huang, M. Dehghan, O. Zaiane, M. Jagersand. "U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection". Pattern Recognition, 106, October 2020.

Keywords: Salient object detection, Convolutional neural network, Network architecture design, Nested U-structure, Multi-scale feature extraction
Category: In Journal
Web Links: Elsevier

BibTeX

@article{Qin+al:20,
  author = {Xuebin Qin and Zichen Zhang and Chenyang Huang and Masood Dehghan
    and Osmar R. Zaiane and Martin Jagersand},
  title = {U2-Net: Going Deeper with Nested U-Structure for Salient Object
    Detection},
  Volume = "106",
  journal = {Pattern Recognition},
  year = 2020,
}

Last Updated: September 15, 2020
Submitted by Sabina P

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