Automatic detection of facial Action Units (AUs) allows for objective facial
expression analysis. Due to the high cost of AU labeling and the limited size
of existing benchmarks, previous AU detection methods tend to overfit the
dataset, resulting in a significant performance loss when evaluated across
corpora. To address this problem, we propose FG-Net for generalizable facial
action unit detection. Specifically, FG-Net extracts feature maps from a
StyleGAN2 model pre-trained on a large and diverse face image dataset. Then,
these features are used to detect AUs with a Pyramid CNN Interpreter, making
the training efficient and capturing essential local features. The proposed
FG-Net achieves a strong generalization ability for heatmap-based AU detection
thanks to the generalizable and semantic-rich features extracted from the
pre-trained generative model. Extensive experiments are conducted to evaluate
within- and cross-corpus AU detection with the widely-used DISFA and BP4D
datasets. Compared with the state-of-the-art, the proposed method achieves
superior cross-domain performance while maintaining competitive within-domain
performance. In addition, FG-Net is data-efficient and achieves competitive
performance even when trained on 1000 samples. Our code will be released at
https://github.com/ihp-lab/FG-Net

通过使用预训练的生成模型提取可泛化和语义丰富的特征，在 AU 检测中取得了强大的泛化能力。在广泛使用的 DISFA 和 BP4D 数据集上进行了大量实验，与现有方法相比，所提出的方法在跨领域性能上达到了优越水平，并保持了在同领域性能上的竞争性。

FG-Net：具有通用金字塔特征的面部动作单位检测

FG-Net: Facial Action Unit Detection with Generalizable Pyramidal  Features

Product attribute-value identification (PAVI) has been studied to link
products on e-commerce sites with their attribute values (e.g., <Material,
Cotton>) using product text as clues. Technical demands from real-world
e-commerce platforms require PAVI methods to handle unseen values,
multi-attribute values, and canonicalized values, which are only partly
addressed in existing extraction- and classification-based approaches.
Motivated by this, we explore a generative approach to the PAVI task. We
finetune a pre-trained generative model, T5, to decode a set of attribute-value
pairs as a target sequence from the given product text. Since the attribute
value pairs are unordered set elements, how to linearize them will matter; we,
thus, explore methods of composing an attribute-value pair and ordering the
pairs for the task. Experimental results confirm that our generation-based
approach outperforms the existing extraction and classification-based methods
on large-scale real-world datasets meant for those methods.

本研究探讨了使用生成模型的产品属性值识别（PAVI）任务，通过微调预训练的 T5 模型，可以在真实世界数据集上优化现有的提取和分类方法。