A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention
Research on text-to-image has gained significant attention. However, existing methods primarily rely on upsampling convolution operations for feature extraction during the initial image generation stage. This approach has inherent limitations, often leading to the loss of global information and the...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119635/ |
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| author | Yang Yang Ainuddin Wahid Bin Abdul Wahab Norisma Binti Idris Dingguo Yu Chang Liu |
| author_facet | Yang Yang Ainuddin Wahid Bin Abdul Wahab Norisma Binti Idris Dingguo Yu Chang Liu |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | Research on text-to-image has gained significant attention. However, existing methods primarily rely on upsampling convolution operations for feature extraction during the initial image generation stage. This approach has inherent limitations, often leading to the loss of global information and the inability to capture long-range semantic dependencies. To address these issues, this study proposes a generation algorithm for “text to image” based on multi-channel attention (TTI-MCA). The method integrates a self-supervised module into the initial image generation phase, leveraging attention mechanisms to enable autonomous mapping learning between image features. This facilitates a deep integration of contextual understanding and self-attention learning. Additionally, a feature fusion enhancement module is introduced, which combines low-resolution features from the previous stage with high-resolution features from the current stage. This allows the generation network to fully utilize the rich semantic information of low-level features and the high-resolution details of high-level features, ultimately producing high-quality, realistic images. Experimental results show that TTI-MCA outperforms the baseline algorithm in both Inception Score (IS) and Fréchet Inception Distance (FID), achieving superior performance on the CUB and COCO datasets. This research provides a novel approach to generating high-quality images from text. |
| format | Article |
| id | doaj-art-c6e98687d8eb4e53a834b1f8a9f28a45 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c6e98687d8eb4e53a834b1f8a9f28a452025-08-25T23:11:53ZengIEEEIEEE Access2169-35362025-01-011314487814488610.1109/ACCESS.2025.359689411119635A Generation Algorithm for “Text to Image” Based on Multi-Channel AttentionYang Yang0Ainuddin Wahid Bin Abdul Wahab1https://orcid.org/0000-0003-1062-0329Norisma Binti Idris2https://orcid.org/0000-0002-8006-7496Dingguo Yu3https://orcid.org/0000-0001-6701-6451Chang Liu4https://orcid.org/0000-0003-0846-956XFaculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaFaculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaFaculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaCollege of Media Engineering, Communication University of Zhejiang, Hangzhou, ChinaCollege of Media Engineering, Communication University of Zhejiang, Hangzhou, ChinaResearch on text-to-image has gained significant attention. However, existing methods primarily rely on upsampling convolution operations for feature extraction during the initial image generation stage. This approach has inherent limitations, often leading to the loss of global information and the inability to capture long-range semantic dependencies. To address these issues, this study proposes a generation algorithm for “text to image” based on multi-channel attention (TTI-MCA). The method integrates a self-supervised module into the initial image generation phase, leveraging attention mechanisms to enable autonomous mapping learning between image features. This facilitates a deep integration of contextual understanding and self-attention learning. Additionally, a feature fusion enhancement module is introduced, which combines low-resolution features from the previous stage with high-resolution features from the current stage. This allows the generation network to fully utilize the rich semantic information of low-level features and the high-resolution details of high-level features, ultimately producing high-quality, realistic images. Experimental results show that TTI-MCA outperforms the baseline algorithm in both Inception Score (IS) and Fréchet Inception Distance (FID), achieving superior performance on the CUB and COCO datasets. This research provides a novel approach to generating high-quality images from text.https://ieeexplore.ieee.org/document/11119635/AI-generated imagesimage feature fusionlong-range semantic dependenciestext-to-image |
| spellingShingle | Yang Yang Ainuddin Wahid Bin Abdul Wahab Norisma Binti Idris Dingguo Yu Chang Liu A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention IEEE Access AI-generated images image feature fusion long-range semantic dependencies text-to-image |
| title | A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention |
| title_full | A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention |
| title_fullStr | A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention |
| title_full_unstemmed | A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention |
| title_short | A Generation Algorithm for “Text to Image” Based on Multi-Channel Attention |
| title_sort | generation algorithm for x201c text to image x201d based on multi channel attention |
| topic | AI-generated images image feature fusion long-range semantic dependencies text-to-image |
| url | https://ieeexplore.ieee.org/document/11119635/ |
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