论文标题
绘画中的材料（MIP）：跨学科数据集，用于感知，艺术历史和计算机视觉
Materials In Paintings (MIP): An interdisciplinary dataset for perception, art history, and computer vision
论文作者
论文摘要
画家可以自由修改自然场景的描述方式，这可能会导致远端世界上令人信服的形象。这标志着照片和绘画之间的主要区别：绘画是为人类感知创造的。研究这些绘画的描述可能对多学科受众有益。在本文中，我们捕获并探索了材料的绘画描绘，以通过艺术家的眼光研究和研究材料的描述和感知。我们注释了一个19k绘画的数据集，其中有200k+边界框，从中自动提取多边形段。为每个边界盒分配一个粗标签（例如织物）和细粒的标签（例如，天鹅绒，丝滑）。我们通过在艺术史，人类的看法和计算机视觉上介绍新发现来证明数据集的跨学科实用性。我们的实验包括分析绘画中描绘的材料的分布，展示画家如何使用风格化的方法创建令人信服的描述，并演示如何使用绘画来构建更强大的计算机视觉模型。我们得出的结论是，我们的绘画材料描述数据集是获得对跨多个学科材料的描绘和感知的丰富来源。 MIP数据集可在https://materialsinpaintings.tudelft.nl上自由访问
A painter is free to modify how components of a natural scene are depicted, which can lead to a perceptually convincing image of the distal world. This signals a major difference between photos and paintings: paintings are explicitly created for human perception. Studying these painterly depictions could be beneficial to a multidisciplinary audience. In this paper, we capture and explore the painterly depictions of materials to enable the study of depiction and perception of materials through the artists' eye. We annotated a dataset of 19k paintings with 200k+ bounding boxes from which polygon segments were automatically extracted. Each bounding box was assigned a coarse label (e.g., fabric) and a fine-grained label (e.g., velvety, silky). We demonstrate the cross-disciplinary utility of our dataset by presenting novel findings across art history, human perception, and computer vision. Our experiments include analyzing the distribution of materials depicted in paintings, showing how painters create convincing depictions using a stylized approach, and demonstrating how paintings can be used to build more robust computer vision models. We conclude that our dataset of painterly material depictions is a rich source for gaining insights into the depiction and perception of materials across multiple disciplines. The MIP dataset is freely accessible at https://materialsinpaintings.tudelft.nl
