Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns
IntroductionSegmentation tasks in computer vision play a crucial role in various applications, ranging from object detection to medical imaging and cultural heritage preservation. Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achie...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1513488/full |
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| author | Lv Yongyin Yu Caixia |
| author_facet | Lv Yongyin Yu Caixia |
| author_sort | Lv Yongyin |
| collection | DOAJ |
| description | IntroductionSegmentation tasks in computer vision play a crucial role in various applications, ranging from object detection to medical imaging and cultural heritage preservation. Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achieved significant success; however, they often face challenges in capturing fine-grained details and maintaining efficiency across diverse datasets. These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.MethodsTo address these limitations, we propose a novel segmentation model that integrates a hierarchical vision transformer backbone with multi-scale self-attention, cascaded attention decoding, and diffusion-based robustness enhancement. Our approach aims to capture both local details and global contexts effectively while maintaining lower computational overhead.Results and discussionExperiments conducted on four diverse datasets, including Ancient Architecture, MS COCO, Cityscapes, and ScanNet, demonstrate that our model outperforms state-of-the-art methods in accuracy, recall, and computational efficiency. The results highlight the model's ability to generalize well across different tasks and provide robust segmentation, even in challenging scenarios. Our work paves the way for more efficient and precise segmentation techniques, making it valuable for applications where both detail and speed are critical. |
| format | Article |
| id | doaj-art-e502656c2cca4f54ac74956613c71c92 |
| institution | Kabale University |
| issn | 1662-5218 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-e502656c2cca4f54ac74956613c71c922025-01-03T11:17:29ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182024-12-011810.3389/fnbot.2024.15134881513488Cross-attention swin-transformer for detailed segmentation of ancient architectural color patternsLv YongyinYu CaixiaIntroductionSegmentation tasks in computer vision play a crucial role in various applications, ranging from object detection to medical imaging and cultural heritage preservation. Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achieved significant success; however, they often face challenges in capturing fine-grained details and maintaining efficiency across diverse datasets. These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.MethodsTo address these limitations, we propose a novel segmentation model that integrates a hierarchical vision transformer backbone with multi-scale self-attention, cascaded attention decoding, and diffusion-based robustness enhancement. Our approach aims to capture both local details and global contexts effectively while maintaining lower computational overhead.Results and discussionExperiments conducted on four diverse datasets, including Ancient Architecture, MS COCO, Cityscapes, and ScanNet, demonstrate that our model outperforms state-of-the-art methods in accuracy, recall, and computational efficiency. The results highlight the model's ability to generalize well across different tasks and provide robust segmentation, even in challenging scenarios. Our work paves the way for more efficient and precise segmentation techniques, making it valuable for applications where both detail and speed are critical.https://www.frontiersin.org/articles/10.3389/fnbot.2024.1513488/fullsegmentationvision transformermulti-scale attentionrobustness enhancementcomputational efficiency |
| spellingShingle | Lv Yongyin Yu Caixia Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns Frontiers in Neurorobotics segmentation vision transformer multi-scale attention robustness enhancement computational efficiency |
| title | Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns |
| title_full | Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns |
| title_fullStr | Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns |
| title_full_unstemmed | Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns |
| title_short | Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns |
| title_sort | cross attention swin transformer for detailed segmentation of ancient architectural color patterns |
| topic | segmentation vision transformer multi-scale attention robustness enhancement computational efficiency |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1513488/full |
| work_keys_str_mv | AT lvyongyin crossattentionswintransformerfordetailedsegmentationofancientarchitecturalcolorpatterns AT yucaixia crossattentionswintransformerfordetailedsegmentationofancientarchitecturalcolorpatterns |