论文标题
艺术绘画中的语义细分
Semantic Segmentation in Art Paintings
论文作者
论文摘要
语义细分是一项艰巨的任务，即使在照片上接受监督的方式进行培训。在本文中，我们解决了艺术绘画的语义分割问题，这是一项更具挑战性的任务，因为颜色，纹理和形状的多样性更大，并且由于没有可用于细分的基础真理注释。我们提出了一种使用域适应性的无监督方法，用于对绘画进行语义分割。我们的方法通过在Pascal VOC 2012数据集中使用样式转移，在特定的艺术风格中创建了一系列伪绘制，然后在Pascal VOC 2012和真实绘画之间应用域混淆。这两个步骤建立在我们收集的新数据集上，称为DRAM（艺术运动中的多样化现实主义），该数据由四个动作的象征性艺术绘画组成，它们的图案，色彩和几何形状高度多样化。为了细分新绘画，我们提出了一种复合的多域适应方法，该方法分别在每个子域上训练，并在推理期间构成其溶液。我们的方法不仅提供了DRAM的特定艺术运动，而且还提供了其他看不见的艺术运动的更好的细分结果。我们比较了我们的替代方法的方法，并显示了语义分割在艺术绘画中的应用。我们方法的代码和模型可公开可用：https：//github.com/nadavc220/semanticsementementationinartpaintings。
Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintings, an even more challenging task because of a much larger diversity in colors, textures, and shapes and because there are no ground truth annotations available for segmentation. We propose an unsupervised method for semantic segmentation of paintings using domain adaptation. Our approach creates a training set of pseudo-paintings in specific artistic styles by using style-transfer on the PASCAL VOC 2012 dataset, and then applies domain confusion between PASCAL VOC 2012 and real paintings. These two steps build on a new dataset we gathered called DRAM (Diverse Realism in Art Movements) composed of figurative art paintings from four movements, which are highly diverse in pattern, color, and geometry. To segment new paintings, we present a composite multi-domain adaptation method that trains on each sub-domain separately and composes their solutions during inference time. Our method provides better segmentation results not only on the specific artistic movements of DRAM, but also on other, unseen ones. We compare our approach to alternative methods and show applications of semantic segmentation in art paintings. The code and models for our approach are publicly available at: https://github.com/Nadavc220/SemanticSegmentationInArtPaintings.
