ESNet: An Efficient Framework for Superpixel Segmentation

Xu, Sen; Wei, Shikui; Ruan, Tao; Zhao, Yao

doi:10.1109/tcsvt.2023.3347402

IEEE Trans. Circuits Syst. Video Technol.

2024

DOI: 10.1109/tcsvt.2023.3347402

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ESNet: An Efficient Framework for Superpixel Segmentation

Sen Xu,

Shikui Wei,

Tao Ruan

et al.

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Cited by 3 publications

References 53 publications

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SMANet: Superpixel-guided multi-scale attention network for medical image segmentation

Shen,

Guo,

Liu

et al. 2025

Biomedical Signal Processing and Control

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SMANet: Superpixel-guided multi-scale attention network for medical image segmentation

Shen,

Guo,

Liu

et al. 2025

Biomedical Signal Processing and Control

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Superpixel-Guided Multi-Type Rail Segmentation via Contextual Information Aggregation

Ni,

Fieguth,

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation

Yan,

Wei,

Jia

et al. 2024

Symmetry

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Superpixels, as essential mid-level image representations, have been widely used in computer vision due to their computational efficiency and redundant compression. Compared with traditional superpixel methods, superpixel algorithms based on deep learning frameworks demonstrate significant advantages in segmentation accuracy. However, existing deep learning-based superpixel algorithms suffer from a loss of details due to convolution and upsampling operations in their encoder–decoder structure, which weakens their semantic detection capabilities. To overcome these limitations, we propose a novel superpixel segmentation network based on a multi-attention hybrid network (MAS-Net). MAS-Net is still based on an efficient symmetric encoder–decoder architecture. First, utilizing residual structure based on a parameter-free attention module at the feature encoding stage enhanced the capture of fine-grained features. Second, adoption of a global semantic fusion self-attention module was used at the feature selection stage to reconstruct the feature map. Finally, fusing the channel with the spatial attention mechanism at the feature-decoding stage was undertaken to obtain superpixel segmentation results with enhanced boundary adherence. Experimental results on real-world image datasets demonstrated that the proposed method achieved competitive results in terms of visual quality and metrics, such as ASA and BR-BP, compared with the state-of-the-art approaches.

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ESNet: An Efficient Framework for Superpixel Segmentation

Cited by 3 publications

References 53 publications

SMANet: Superpixel-guided multi-scale attention network for medical image segmentation

SMANet: Superpixel-guided multi-scale attention network for medical image segmentation

Superpixel-Guided Multi-Type Rail Segmentation via Contextual Information Aggregation

MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation

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