We introduce a novel image-to-painting method that facilitates the creation
of large-scale, high-fidelity paintings with human-like quality and stylistic
variation. To process large images and gain control over the painting process,
we introduce a segmentation-based painting process and a dynamic attention map
approach inspired by human painting strategies, allowing optimization of brush
strokes to proceed in batches over different image regions, thereby capturing
both large-scale structure and fine details, while also allowing stylistic
control over detail. Our optimized batch processing and patch-based loss
framework enable efficient handling of large canvases, ensuring our painted
outputs are both aesthetically compelling and functionally superior as compared
to previous methods, as confirmed by rigorous evaluations. Code available at:
this https URL

我们介绍了一种新颖的图像到绘画的方法，它能够以人类般的质量和风格变化创造大规模、高保真度的绘画作品。通过引入基于分割的绘画过程和受人类绘画策略启发的动态注意力地图方法，我们可以处理大尺寸的图像并对绘画过程进行控制，从而在不同图像区域上进行刷画优化，捕捉到大尺度的结构和细节，同时也允许对细节进行风格控制。我们优化的批处理和基于补丁的损失框架实现了对大画布的高效处理，确保我们的绘画作品在审美上具有吸引力，并在功能上优于以前的方法，经过了严格的评估。可在以下网址找到代码：this https URL

基于分割的参数化绘画

Segmentation-Based Parametric Painting

Stylistic variation in text needs to be studied with different aspects
including the writer's personal traits, interpersonal relations, rhetoric, and
more. Despite recent attempts on computational modeling of the variation, the
lack of parallel corpora of style language makes it difficult to systematically
control the stylistic change as well as evaluate such models. We release
PASTEL, the parallel and annotated stylistic language dataset, that contains
~41K parallel sentences (8.3K parallel stories) annotated across different
personas. Each persona has different styles in conjunction: gender, age,
country, political view, education, ethnic, and time-of-writing. The dataset is
collected from human annotators with solid control of input denotation: not
only preserving original meaning between text, but promoting stylistic
diversity to annotators. We test the dataset on two interesting applications of
style language, where PASTEL helps design appropriate experiment and
evaluation. First, in predicting a target style (e.g., male or female in
gender) given a text, multiple styles of PASTEL make other external style
variables controlled (or fixed), which is a more accurate experimental design.
Second, a simple supervised model with our parallel text outperforms the
unsupervised models using nonparallel text in style transfer. Our dataset is
publicly available.

通过发布一个包含不同身份的 8.3K 个句子集合 PASTEL，我们可以控制文体的改变并且更准确的评估和设计目标样式和风格转移实验。

男性本科生和女性博士生具有不同的含义：具有多个人格的平行注释风格语言数据集

(Male, Bachelor) and (Female, Ph.D) have different connotations:  Parallelly Annotated Stylistic Language Dataset with Multiple Personas

Inspired by Labov's seminal work on stylistic variation as a function of
social stratification, we develop and compare neural models that predict a
person's presumed socio-economic status, obtained through distant
supervision,from their writing style on social media. The focus of our work is
on identifying the most important stylistic parameters to predict
socio-economic group. In particular, we show the effectiveness of
morpho-syntactic features as stylistic predictors of socio-economic group,in
contrast to lexical features, which are good predictors of topic.

研究社会阶层的影响因素，使用神经模型预测社交媒体上个人的社会经济地位，探讨语言风格在预测社会经济群体方面的作用，发现新颖的词法 - 句法特征是一种有效的风格预测因素。