Generative AIs produce creative outputs in the style of human expression. We
argue that encounters with the outputs of modern generative AI models are
mediated by the same kinds of aesthetic judgments that organize our
interactions with artwork. The interpretation procedure we use on art we find
in museums is not an innate human faculty, but one developed over history by
disciplines such as art history and art criticism to fulfill certain social
functions. This gives us pause when considering our reactions to generative AI,
how we should approach this new medium, and why generative AI seems to incite
so much fear about the future. We naturally inherit a conundrum of causal
inference from the history of art: a work can be read as a symptom of the
cultural conditions that influenced its creation while simultaneously being
framed as a timeless, seemingly acausal distillation of an eternal human
condition. In this essay, we focus on an unresolved tension when we bring this
dilemma to bear in the context of generative AI: are we looking for proof that
generated media reflects something about the conditions that created it or some
eternal human essence? Are current modes of interpretation sufficient for this
task? Historically, new forms of art have changed how art is interpreted, with
such influence used as evidence that a work of art has touched some essential
human truth. As generative AI influences contemporary aesthetic judgment we
outline some of the pitfalls and traps in attempting to scrutinize what AI
generated media means.

生成人工智能产生以人类表达风格为基础的创造性产出，我们认为现代生成人工智能模型的输出所引起的体验是通过组织我们与艺术作品的互动的审美判断来进行的。

人工智能与美学判断

Artificial Intelligence and Aesthetic Judgment

The search for specific objects or motifs is essential to art history as both
assist in decoding the meaning of artworks. Digitization has produced large art
collections, but manual methods prove to be insufficient to analyze them. In
the following, we introduce an algorithm that allows users to search for image
regions containing specific motifs or objects and find similar regions in an
extensive dataset, helping art historians to analyze large digitized art
collections. Computer vision has presented efficient methods for visual
instance retrieval across photographs. However, applied to art collections,
they reveal severe deficiencies because of diverse motifs and massive domain
shifts induced by differences in techniques, materials, and styles. In this
paper, we present a multi-style feature fusion approach that successfully
reduces the domain gap and improves retrieval results without labelled data or
curated image collections. Our region-based voting with GPU-accelerated
approximate nearest-neighbour search allows us to find and localize even small
motifs within an extensive dataset in a few seconds. We obtain state-of-the-art
results on the Brueghel dataset and demonstrate its generalization to
inhomogeneous collections with a large number of distractors.

本文介绍了一种基于计算机视觉的算法，利用多样性特征融合和区域投票技术可以准确地搜索和定位艺术品中指定的图案或物体，有效地解决了大规模数字化艺术品数据集的图像检索问题。

利用深度多风格特征融合和迭代投票在大型艺术收藏中进行物体检索和定位

Object Retrieval and Localization in Large Art Collections using Deep  Multi-Style Feature Fusion and Iterative Voting

How does the machine classify styles in art? And how does it relate to art
historians' methods for analyzing style? Several studies have shown the ability
of the machine to learn and predict style categories, such as Renaissance,
Baroque, Impressionism, etc., from images of paintings. This implies that the
machine can learn an internal representation encoding discriminative features
through its visual analysis. However, such a representation is not necessarily
interpretable. We conducted a comprehensive study of several of the
state-of-the-art convolutional neural networks applied to the task of style
classification on 77K images of paintings, and analyzed the learned
representation through correlation analysis with concepts derived from art
history. Surprisingly, the networks could place the works of art in a smooth
temporal arrangement mainly based on learning style labels, without any a
priori knowledge of time of creation, the historical time and context of
styles, or relations between styles. The learned representations showed that
there are few underlying factors that explain the visual variations of style in
art. Some of these factors were found to correlate with style patterns
suggested by Heinrich W\"olfflin (1846-1945). The learned representations also
consistently highlighted certain artists as the extreme distinctive
representative of their styles, which quantitatively confirms art historian
observations.

研究了卷积神经网络在艺术风格分类任务中的应用，并通过与艺术史概念的相关性分析评估了其学习表征。学习表征显示艺术风格的视觉变化可以由少数基本因素解释，这些因素与 Heinrich Wölfflin 提出的风格模式相关，并且网络能在没有关于时间和历史背景等信息下，将艺术品放在一个平滑的时间数组里。