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Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey

Research Article

Triplet Attention Enhanced DeepLab V3+ for Semantic Segmentation: Improving Feature Extraction and Fine-Grained Understanding

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  • @INPROCEEDINGS{10.4108/eai.21-11-2024.2354635,
        author={Zhuoran  Li and Xun  Shu and Yancong  Deng},
        title={Triplet Attention Enhanced DeepLab V3+ for Semantic Segmentation: Improving Feature Extraction and Fine-Grained Understanding},
        proceedings={Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey},
        publisher={EAI},
        proceedings_a={CONF-MLA},
        year={2025},
        month={3},
        keywords={semantic segmentation triplet attention mechanism deeplabv3+ improved feature extraction},
        doi={10.4108/eai.21-11-2024.2354635}
    }
    
  • Zhuoran Li
    Xun Shu
    Yancong Deng
    Year: 2025
    Triplet Attention Enhanced DeepLab V3+ for Semantic Segmentation: Improving Feature Extraction and Fine-Grained Understanding
    CONF-MLA
    EAI
    DOI: 10.4108/eai.21-11-2024.2354635
Zhuoran Li1,*, Xun Shu2, Yancong Deng3
  • 1: City University of Hong Kong
  • 2: Capital University of Economics and Business
  • 3: University of California San Diego
*Contact email: zhuorali2-c@my.cityu.edu.hk

Abstract

Semantic segmentation is a critical task in computer vision that requires assigning class labels to individual pixels for a deeper understanding of visual scenes. This paper explores methods to enhance the DeepLab V3+ model by integrating attention mechanisms, specifically Triplet Attention and CBAM, into the backbone, ASPP module, and decoder. We conducted extensive experiments using the Cityscapes dataset to assess the impact of these attention mechanisms. Our results demonstrate that Triplet Attention, particularly when applied to the backbone, significantly improves segmentation performance with minimal computational overhead, outperforming CBAM in most configurations. This study highlights the effectiveness of attention mechanisms in improving the precision and semantic understanding of segmentation models. Future work will explore additional attention mechanisms and expand their application to broader vision-related tasks.

Keywords
semantic segmentation triplet attention mechanism deeplabv3+ improved feature extraction
Published
2025-03-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.21-11-2024.2354635
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