Capturing the diversity of people in images is challenging: recent literature
tends to focus on diversifying one or two attributes, requiring expensive
attribute labels or building classifiers. We introduce a diverse people image
ranking method which more flexibly aligns with human notions of people
diversity in a less prescriptive, label-free manner. The Perception-Aligned
Text-derived Human representation Space (PATHS) aims to capture all or many
relevant features of people-related diversity, and, when used as the
representation space in the standard Maximal Marginal Relevance (MMR) ranking
algorithm, is better able to surface a range of types of people-related
diversity (e.g. disability, cultural attire). PATHS is created in two stages.
First, a text-guided approach is used to extract a person-diversity
representation from a pre-trained image-text model. Then this representation is
fine-tuned on perception judgments from human annotators so that it captures
the aspects of people-related similarity that humans find most salient.
Empirical results show that the PATHS method achieves diversity better than
baseline methods, according to side-by-side ratings from human annotators.

将多样化的人们图像排名与人类的人们多样性概念相匹配的一种方法，使用 Perception-Aligned Text-derived Human representation Space（PATHS）作为表示空间，在标准的最大边际相关（MMR）排名算法中更能展现人们多样性的不同类型（如残疾和文化服饰），经验证实现了比基准方法更好的多样性。

广义人群多样性：学习与人类感知对齐的人群图像多样性表示

Generalized People Diversity: Learning a Human Perception-Aligned  Diversity Representation for People Images

In this work, we explore the challenging task of generating 3D shapes from
text. Beyond the existing works, we propose a new approach for text-guided 3D
shape generation, capable of producing high-fidelity shapes with colors that
match the given text description. This work has several technical
contributions. First, we decouple the shape and color predictions for learning
features in both texts and shapes, and propose the word-level spatial
transformer to correlate word features from text with spatial features from
shape. Also, we design a cyclic loss to encourage consistency between text and
shape, and introduce the shape IMLE to diversify the generated shapes. Further,
we extend the framework to enable text-guided shape manipulation. Extensive
experiments on the largest existing text-shape benchmark manifest the
superiority of this work. The code and the models are available at
this https URL Text-Guided-Shape-Generation.

该研究探讨了从文本生成 3D 形状的任务，提出了一种新的文本引导的 3D 形状生成方法，能够在形状与颜色上产生高保真匹配文本描述的形状，技术贡献包括基于词级空间转换器和循环损失的一致性措施，并引入了形状 IMLE 进行生成的多样性。