Semi-supervised reference-based sketch extraction using a contrastive learning framework
<|reference_start|>Semi-supervised reference-based sketch extraction using a contrastive learning framework: 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.<|reference_end|>
@article{seo2024semi-supervised,
    title={Semi-supervised reference-based sketch extraction using a contrastive
  learning framework},
    author={Chang Wook Seo, Amirsaman Ashtari, Junyong Noh},
    journal={ACM Transactions on Graphics (TOG) 2023, Volume 42, Issue 4
  Article No.: 56, Pages 1 - 12},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14026},
    primaryClass={cs.CV}
}