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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14026 |
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| _version_ | 1866911961882558464 |
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| author | Seo, Chang Wook Ashtari, Amirsaman Noh, Junyong |
| author_facet | Seo, Chang Wook Ashtari, Amirsaman Noh, Junyong |
| contents | Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semi-supervised reference-based sketch extraction using a contrastive learning framework Seo, Chang Wook Ashtari, Amirsaman Noh, Junyong Computer Vision and Pattern Recognition Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations. |
| title | Semi-supervised reference-based sketch extraction using a contrastive learning framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.14026 |