论文标题
自动编码转换的自我训练合奏的艺术风格分类
Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations
论文作者
论文摘要
一幅画的艺术风格是一位丰富的描述符，它揭示了有关艺术家如何独特地描绘和表达其创造力的视觉和深刻内在知识。对不同艺术运动和样式的绘画进行准确的分类对于大规模的艺术数据库索引至关重要。但是，在计算机视觉研究领域，自动提取和认可这些高度密集的艺术特征几乎没有受到关注。在本文中，我们研究了深度自学学习方法的使用来解决识别具有较高阶层和较低阶层变化的复杂艺术风格的问题。此外，在具有27种艺术类别的高度不平衡的Wikiart数据集上，我们的表现要优于现有方法几乎20％。为了实现这一目标，我们使用有限的注释数据样本来训练ENAET半监督学习模型（Wang等，2019），并用从空间和非空间转换集合中学到的自我监督的表示。
The artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision. Accurate categorization of paintings across different artistic movements and styles is critical for large-scale indexing of art databases. However, the automatic extraction and recognition of these highly dense artistic features has received little to no attention in the field of computer vision research. In this paper, we investigate the use of deep self-supervised learning methods to solve the problem of recognizing complex artistic styles with high intra-class and low inter-class variation. Further, we outperform existing approaches by almost 20% on a highly class imbalanced WikiArt dataset with 27 art categories. To achieve this, we train the EnAET semi-supervised learning model (Wang et al., 2019) with limited annotated data samples and supplement it with self-supervised representations learned from an ensemble of spatial and non-spatial transformations.
