  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.
