The duality of content and style is inherent to the nature of art. For
humans, these two elements are clearly different: content refers to the objects
and concepts in the piece of art, and style to the way it is expressed. This
duality poses an important challenge for computer vision. The visual appearance
of objects and concepts is modulated by the style that may reflect the author's
emotions, social trends, artistic movement, etc., and their deep comprehension
undoubtfully requires to handle both. A promising step towards a general
paradigm for art analysis is to disentangle content and style, whereas relying
on human annotations to cull a single aspect of artworks has limitations in
learning semantic concepts and the visual appearance of paintings. We thus
present GOYA, a method that distills the artistic knowledge captured in a
recent generative model to disentangle content and style. Experiments show that
synthetically generated images sufficiently serve as a proxy of the real
distribution of artworks, allowing GOYA to separately represent the two
elements of art while keeping more information than existing methods.

GOYA 是一种基于生成模型的绘画内容与风格分离的艺术分析方法，实验表明其可以更好地获取艺术作品的语义概念与视觉外观信息。

不仅是生成艺术：稳定扩散用于艺术分析中内容 - 风格解缠

Not Only Generative Art: Stable Diffusion for Content-Style  Disentanglement in Art Analysis

Transfer learning from huge natural image datasets, fine-tuning of deep
neural networks and the use of the corresponding pre-trained networks have
become de facto the core of art analysis applications. Nevertheless, the
effects of transfer learning are still poorly understood. In this paper, we
first use techniques for visualizing the network internal representations in
order to provide clues to the understanding of what the network has learned on
artistic images. Then, we provide a quantitative analysis of the changes
introduced by the learning process thanks to metrics in both the feature and
parameter spaces, as well as metrics computed on the set of maximal activation
images. These analyses are performed on several variations of the transfer
learning procedure. In particular, we observed that the network could
specialize some pre-trained filters to the new image modality and also that
higher layers tend to concentrate classes. Finally, we have shown that a double
fine-tuning involving a medium-size artistic dataset can improve the
classification on smaller datasets, even when the task changes.

该研究论文探究了通过可视化网络内部表征和量化学习过程中引入的变化（包括特征空间、参数空间和最大激活图像集合的度量）的方式来理解神经网络在艺术图像上所学习的内容，同时展示了一种双重微调策略可以提高较小数据集上的艺术图像分类性能。

卷积神经网络在艺术图像中迁移学习的分析

An analysis of the transfer learning of convolutional neural networks  for artistic images

Image compositions as a tool for analysis of artworks is of extreme
significance for art historians. These compositions are useful in analyzing the
interactions in an image to study artists and their artworks. Max Imdahl in his
work called Ikonik, along with other prominent art historians of the 20th
century, underlined the aesthetic and semantic importance of the structural
composition of an image. Understanding underlying compositional structures
within images is challenging and a time consuming task. Generating these
structures automatically using computer vision techniques (1) can help art
historians towards their sophisticated analysis by saving lot of time;
providing an overview and access to huge image repositories and (2) also
provide an important step towards an understanding of man made imagery by
machines. In this work, we attempt to automate this process using the existing
state of the art machine learning techniques, without involving any form of
training. Our approach, inspired by Max Imdahl's pioneering work, focuses on
two central themes of image composition: (a) detection of action regions and
action lines of the artwork; and (b) pose-based segmentation of foreground and
background. Currently, our approach works for artworks comprising of
protagonists (persons) in an image. In order to validate our approach
qualitatively and quantitatively, we conduct a user study involving experts and
non-experts. The outcome of the study highly correlates with our approach and
also demonstrates its domain-agnostic capability. We have open-sourced the code
at this https URL

本研究尝试使用当前最先进的机器学习技术自动化艺术品图像构图的分析，通过检测艺术品中的行动区域和动作线以及前景和背景之间的基于姿势的分割，为艺术史学家提供更加高效和精细的分析方法，并且可以使机器更好地理解人类创造的艺术品。