论文标题
用于对衣服图像的基准，具有不规则的孔
A Benchmark for Inpainting of Clothing Images with Irregular Holes
论文作者
论文摘要
时尚形象的理解是一个活跃的研究领域，该领域为行业提供了大量实际应用。尽管对智能时尚分析系统产生了实际影响，但尚未对服装图像进行介绍。为此，我们介绍了众所周知的时尚数据集上介绍服装图像的广泛基准。此外，我们介绍了局部卷积的扩张版本的使用，该版本有效地得出了掩码更新步骤，并从经验上表明，所提出的方法减少了所需的层数以形成完全透明的掩码。实验表明，与其他填充策略相比，扩张的部分卷积（DPCONV）改善了定量镶嵌性能，尤其是当掩码大小为20％或更多图像时，其性能更好。 \关键字{图像介绍，时尚形象理解，扩张的卷积，部分卷积
Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, which efficiently derive the mask update step, and empirically show that the proposed method reduces the required number of layers to form fully-transparent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image. \keywords{image inpainting, fashion image understanding, dilated convolutions, partial convolutions
