AQ-CSL: Attention Querying Facial Action Unit Detection Net withCross Subject Learning

Abstract Recently, the advancement of deep learning has led to considerable breakthroughs in the automated detection of ActionUnits (AUs). Nevertheless, this field is still faced with various challenges, including limited subjects in shared datasetsand the difficulty of collecting AU data as domain knowledge required for annotating AUs. These issues make it arduousfor the model to generalize across different subjects and attain satisfactory performance on all AUs. To address thesechallenges, we propose two methods, namely AU-specific Querying (AQ) and Feedback Querying (FQ). AQ learns theglobal semantics of a particular AU, while FQ provides local structure information related to the AU semantic vector.The combination of these two operations enables the model to leverage both local features and global semantics of AUs.Moreover, Feedback Querying exhibits strong extensibility, which has led us to propose Cross-subject Querying (CQ).This method learns a subject-independent feature representation for each AU, resulting in improved generalizationability across different subjects. We demonstrate the effectiveness of our methods through visual presentations andablation analysis. By combining all the strategies, our proposed AQ-CSL becomes the state-of-the-art model on theDISFA and BP4D datasets.