Since the launch of applications such as DALL-E, Midjourney, and Stable
Diffusion, generative artificial intelligence has been controversial as a tool
for creating artwork. While some have presented longtermist worries about these
technologies as harbingers of fully automated futures to come, more pressing is
the impact of generative AI on creative labour in the present. Already,
business leaders have begun replacing human artistic labour with AI-generated
images. In response, the artistic community has launched a protest movement,
which argues that AI image generation is a kind of theft. This paper analyzes,
substantiates, and critiques these arguments, concluding that AI image
generators involve an unethical kind of labour theft. If correct, many other AI
applications also rely upon theft.

通过分析、证实和批判这些论点，本文得出结论，认为 AI 图像生成器涉及一种不道德的劳动盗窃行为。如果正确，许多其他 AI 应用也依赖于盗窃。

AI 艺术是偷窃：劳动、剥夺与剥削之危险或随机波洛克的危害

AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the  Dangers of Stochastic Pollocks

We present a novel framework for multi-domain synthesis of artwork from
semantic layouts. One of the main limitations of this challenging task is the
lack of publicly available segmentation datasets for art synthesis. To address
this problem, we propose a dataset, which we call ArtSem, that contains 40,000
images of artwork from 4 different domains with their corresponding semantic
label maps. We generate the dataset by first extracting semantic maps from
landscape photography and then propose a conditional Generative Adversarial
Network (GAN)-based approach to generate high-quality artwork from the semantic
maps without necessitating paired training data. Furthermore, we propose an
artwork synthesis model that uses domain-dependent variational encoders for
high-quality multi-domain synthesis. The model is improved and complemented
with a simple but effective normalization method, based on normalizing both the
semantic and style jointly, which we call Spatially STyle-Adaptive
Normalization (SSTAN). In contrast to previous methods that only take semantic
layout as input, our model is able to learn a joint representation of both
style and semantic information, which leads to better generation quality for
synthesizing artistic images. Results indicate that our model learns to
separate the domains in the latent space, and thus, by identifying the
hyperplanes that separate the different domains, we can also perform
fine-grained control of the synthesized artwork. By combining our proposed
dataset and approach, we are able to generate user-controllable artwork that is
of higher quality than existing

我们提出了一个新颖的多领域艺术品合成框架，通过语义布局生成艺术作品。我们还提出了一种名为 ArtSem 的数据集，包含 40,000 张不同领域的艺术品图像及其对应的语义标签地图。利用条件生成对抗网络（GAN），我们在不需要成对训练数据的情况下，从语义地图生成高质量的艺术品。我们的模型能够学习样式和语义信息的结合表示，从而提高生成艺术图像的质量。通过结合数据集和方法，我们能够生成用户可控的高质量艺术作品。

可控多领域语义艺术创作

Controllable Multi-domain Semantic Artwork Synthesis

Existing computer vision research in artwork struggles with artwork's
fine-grained attributes recognition and lack of curated annotated datasets due
to their costly creation. To the best of our knowledge, we are one of the first
methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural
network on a variety of artwork images and text descriptions pairs. CLIP is
able to learn directly from free-form art descriptions, or, if available,
curated fine-grained labels. Model's zero-shot capability allows predicting
accurate natural language description for a given image, without directly
optimizing for the task. Our approach aims to solve 2 challenges: instance
retrieval and fine-grained artwork attribute recognition. We use the iMet
Dataset, which we consider the largest annotated artwork dataset. In this
benchmark we achieved competitive results using only self-supervision.

使用 CLIP 方法训练神经网络， 实现了自由形式的艺术品描述和精细标签的学习，在计算机视觉和艺术品属性识别中具有较高实用价值，以 iMet 数据集为基础，使用自监督学习获得了有竞争力的结果。

CLIP-Art: 微粒级艺术分类的对比式预训练

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

CNNs have massively improved performance in object detection in photographs.
However research into object detection in artwork remains limited. We show
state-of-the-art performance on a challenging dataset, People-Art, which
contains people from photos, cartoons and 41 different artwork movements. We
achieve this high performance by fine-tuning a CNN for this task, thus also
demonstrating that training CNNs on photos results in overfitting for photos:
only the first three or four layers transfer from photos to artwork. Although
the CNN's performance is the highest yet, it remains less than 60\% AP,
suggesting further work is needed for the cross-depiction problem. The final
publication is available at Springer via
this http URL

该研究表明 fine-tune 卷积神经网络在艺术品中进行物体检测的表现具有极高的性能，但仍需要进一步研究来解决跨图像类型检测的问题。