Multi-scale dual self-attention refine network for semantic segmentation

Juan Yang; Zhiyi Huang; Yao Wang; Yin Yang

doi:10.1117/12.2684630

19 October 2023 Multi-scale dual self-attention refine network for semantic segmentation

Juan Yang, Zhiyi Huang, Yao Wang, Yin Yang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090X (2023) https://doi.org/10.1117/12.2684630
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Effective image semantic segmentation is an important task. In recent years, deep convolutional neural networks have played a major role in improving the effectiveness of semantic segmentation. However, previous works capture local contexts by multi-scale feature fusion which ignore the global dependencies. Moreover, as the layers deepen, the loss of feature is inevitable. Multi-Scale Dual Attention Refine Network (MSDARNet) is proposed. Specifically, we firstly append two types of attention modules on top of dilated FCN, which model the global semantic interdependencies in spatial and channel dimensions respectively. Secondly, multi-scale feature fusion is added to extract local dependencies in the above attention module. We conduct extensive experiments to validate the effectiveness of our network on three challenging semantic segmentation datasets and obtain great improvements. In particular, our MSDARNet attains mIoU accurary of 50.3% on NYUDv2, 71.1% on Person-Part.

Citation Download Citation

Juan Yang, Zhiyi Huang, Yao Wang, and Yin Yang "Multi-scale dual self-attention refine network for semantic segmentation", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090X (19 October 2023); https://doi.org/10.1117/12.2684630

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KEYWORDS

Semantics

Image segmentation

Feature fusion

Feature extraction

Ablation

Education and training

Image fusion

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