Abstract
Face parsing aims to segment facial images into key components such as eyes, lips, and eyebrows. While existing methods rely on dense pixel-level annotations, such annotations are expensive and labor-intensive to obtain. To reduce annotation cost, we introduce Weakly Supervised Face Parsing (WSFP), a new task setting that performs dense facial component segmentation using only weak supervision, such as image-level labels and natural language descriptions. WSFP introduces unique challenges due to the high co-occurrence and visual similarity of facial components, which lead to ambiguous activations and degraded parsing performance. To address this, we propose DisFaceRep, a representation disentanglement framework designed to separate co-occurring facial components through both explicit and implicit mechanisms. Specifically, we introduce a co-occurring component disentanglement strategy to explicitly reduce dataset-level bias, and a text-guided component disentanglement loss to guide component separation using language supervision implicitly. Extensive experiments on CelebAMask-HQ, LaPa, and Helen demonstrate the difficulty of WSFP and the effectiveness of DisFaceRep, which significantly outperforms existing weakly supervised semantic segmentation methods. The code will be released at \href{this https URL}{\textcolor{cyan}{this https URL}}.
Abstract (translated)
面部解析的目标是将面部图像分割成关键组成部分，如眼睛、嘴唇和眉毛。现有的方法依赖于密集的像素级注释，但这些注释获取起来成本高昂且费时费力。为了降低标注成本，我们提出了弱监督面部解析（WSFP），这是一种新任务设定，它仅使用诸如图形单标签和自然语言描述之类的弱监督信息来进行稠密的面部组件分割。由于面部组件之间的高度共现性和视觉相似性，WSFP带来了独特的挑战，这会导致模糊激活并降低解析性能。 为了解决这些问题，我们提出了DisFaceRep框架，该框架设计用于通过显式和隐式的机制来分离共存的面部组件。具体来说，我们引入了一种共发生组件解缠策略以减少数据集级别的偏差，并提出了一种文本引导组件解缠损失函数，使用语言监督隐式地指导组件分离。在CelebAMask-HQ、LaPa和Helen等数据集上的大量实验表明了WSFP的难度以及DisFaceRep的有效性，该方法显著优于现有的弱监督语义分割方法。 代码将在[此处](this https URL)发布。
URL
https://arxiv.org/abs/2508.01250
