新数据集揭示影响风格迁移评分的关键因素。
Style Transfer Dataset: What Makes A Good Stylization?
- 构建1万张人工评分的风格化图像数据集
- 发现内容与风格匹配度决定用户评分高低
- 为自动评估与配置提供可量化的依据
我们提出一个新数据集，旨在推进图像风格迁移任务——将一张图像以另一张图像的风格呈现。该数据集涵盖多种尺寸的内容图与风格图，包含10,000次由三位标注者在1-10分制下人工评分的风格化结果。基于评分数据，我们识别出影响用户评价优劣的主要因素，并验证了若干量化指标对用户评分具有统计显著影响。文中还讨论了风格迁移数据集的构建方法。该数据集可用于自动化风格迁移配置与评估等多项任务。
原文摘要 · Abstract (English)
We present a new dataset with the goal of advancing image style transfer - the task of rendering one image in the style of another image. The dataset covers various content and style images of different size and contains 10.000 stylizations manually rated by three annotators in 1-10 scale. Based on obtained ratings, we find which factors are mostly responsible for favourable and poor user evaluations and show quantitative measures having statistically significant impact on user grades. A methodology for creating style transfer datasets is discussed. Presented dataset can be used in automating multiple tasks, related to style transfer configuration and evaluation.
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