基于对齐的学习：应对跨多域面部表情识别中的域间和域内偏移
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
AI总结：
本文提出LA-CMFER框架，通过全局和局部分支提取特征，结合双层域间对齐与多视图域内对齐方法，有效应对跨多域面部表情识别中的域间和域内偏移，在六个基准数据集上验证了其优越性。
摘要：
面部表情识别（FER）在人机交互中具有重要意义。现有的跨域FER方法通常仅从单一有标签源域向无标签目标域迁移知识，忽略了跨多个源域的综合信息。然而，跨多域FER（CMFER）非常具有挑战性，原因在于（i）跨多个域的固有域间偏移，以及（ii）源于模糊表情和低类间区分度的域内偏移。在本文中，我们提出了一种新颖的基于对齐的学习CMFER框架，命名为LA-CMFER，以同时处理域间和域内偏移。具体而言，LA-CMFER由全局分支和局部分支构建，分别从完整图像和局部细微表情中提取特征。基于此，LA-CMFER提出了一种双层域间对齐方法，在样本级别迫使模型在知识迁移中优先处理难对齐样本，同时在聚类级别通过类属性引导逐步生成良好聚类的特征空间，从而缩小域间偏移。为解决域内偏移，LA-CMFER引入了具有多视图聚类一致性约束的多视图域内对齐方法，其中构建了预测相似性矩阵以追求全局和局部视图之间的一致性，从而优化伪标签并消除潜在噪声。在六个基准数据集上的大量实验验证了我们LA-CMFER的优越性。
英文摘要：
Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single labeled source domain to an unlabeled target domain, neglecting the comprehensive information across multiple sources. Nevertheless, cross-multidomain FER (CMFER) is very challenging for (i) the inherent inter-domain shifts across multiple domains and (ii) the intra-domain shifts stemming from the ambiguous expressions and low inter-class distinctions. In this paper, we propose a novel Learning with Alignments CMFER framework, named LA-CMFER, to handle both inter- and intra-domain shifts. Specifically, LA-CMFER is constructed with a global branch and a local branch to extract features from the full images and local subtle expressions, respectively. Based on this, LA-CMFER presents a dual-level inter-domain alignment method to force the model to prioritize hard-to-align samples in knowledge transfer at a sample level while gradually generating a well-clustered feature space with the guidance of class attributes at a cluster level, thus narrowing the inter-domain shifts. To address the intra-domain shifts, LA-CMFER introduces a multi-view intra-domain alignment method with a multi-view clustering consistency constraint where a prediction similarity matrix is built to pursue consistency between the global and local views, thus refining pseudo labels and eliminating latent noise. Extensive experiments on six benchmark datasets have validated the superiority of our LA-CMFER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
