使用对比学习框架的半监督参考素描提取

Chang Wook Seo, Amirsaman Ashtari, Jun-yong Noh
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引用次数: 0

摘要

速写反映了艺术家个人的绘画风格;因此,在为各种应用从彩色图像中提取草图时,考虑它们的独特风格是很重要的。不幸的是,大多数现有的草图提取方法都被设计为提取单一样式的草图。虽然已经有一些尝试生成各种风格的草图,但这些方法通常存在两个局限性:结果质量低,并且由于需要配对数据集而难以训练模型。在本文中,我们提出了一种新的多模态草图提取方法,该方法可以以半监督的方式通过非配对数据训练来模仿给定参考草图的风格。我们的方法在定量和定性评估中都优于最先进的素描提取方法和不成对图像翻译方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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