AI systems rely on extensive training on large datasets to address various
tasks. However, image-based systems, particularly those used for demographic
attribute prediction, face significant challenges. Many current face image
datasets primarily focus on demographic factors such as age, gender, and skin
tone, overlooking other crucial facial attributes like hairstyle and
accessories. This narrow focus limits the diversity of the data and
consequently the robustness of AI systems trained on them. This work aims to
address this limitation by proposing a methodology for generating synthetic
face image datasets that capture a broader spectrum of facial diversity.
Specifically, our approach integrates a systematic prompt formulation strategy,
encompassing not only demographics and biometrics but also non-permanent traits
like make-up, hairstyle, and accessories. These prompts guide a
state-of-the-art text-to-image model in generating a comprehensive dataset of
high-quality realistic images and can be used as an evaluation set in face
analysis systems. Compared to existing datasets, our proposed dataset proves
equally or more challenging in image classification tasks while being much
smaller in size.

通过提出一种生成综合面部多样性数据集的方法，本研究旨在解决现有面部图像数据集集中在年龄、性别和肤色等少数面部属性上的问题，从而提高 AI 系统的鲁棒性。这种方法不仅包括人口统计学和生物识别特征，还涵盖了妆容、发型和配饰等非永久性特征，通过引导最先进的文本到图像模型生成高质量、逼真的综合数据集，并可用于面部分析系统的评估。与现有数据集相比，我们提出的数据集在图像分类任务中同样具有或更高的挑战性，同时大小更小。

SDFD: 构建具有多样属性的通用合成人脸图像数据集

SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse  Attributes

User behavior modeling is important for industrial applications such as
demographic attribute prediction, content recommendation, and target
advertising. Existing methods represent behavior log as a sequence of adopted
items and find sequential patterns; however, concrete location and time
information in the behavior log, reflecting dynamic and periodic patterns,
joint with the spatial dimension, can be useful for modeling users and
predicting their characteristics. In this work, we propose a novel model based
on graph neural networks for learning user representations from spatiotemporal
behavior data. A behavior log comprises a sequence of sessions; and a session
has a location, start time, end time, and a sequence of adopted items. Our
model's architecture incorporates two networked structures. One is a tripartite
network of items, sessions, and locations. The other is a hierarchical calendar
network of hour, week, and weekday nodes. It first aggregates embeddings of
location and items into session embeddings via the tripartite network, and then
generates user embeddings from the session embeddings via the calendar
structure. The user embeddings preserve spatial patterns and temporal patterns
of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It
adopts the attention mechanism to model complex interactions among the multiple
patterns in user behaviors. Experiments on real datasets (i.e., clicks on news
articles in a mobile app) show our approach outperforms strong baselines for
predicting missing demographic attributes.

本研究提出了一种基于图神经网络的新型模型，通过学习用户在时空数据上的行为来建模，使用周期性图可以实现保存用户的空间模式和时空模式，该模型可用于预测不完整的人口属性。