AVF-MAE++: Scaling Affective Video Facial Masked Autoencoders via Efficient Audio-Visual Self-Supervised Learning

Affective Video Facial Analysis (AVFA) is important for advancing emotion-aware AI, yet the persistent data scarcity in AVFA presents challenges. Recently, the self-supervised learning (SSL) technique of Masked Autoencoders (MAE) has gained significant attention, particularly in its audiovisual adaptation. Insights from general domains suggest that scaling is vital for unlocking impressive improvements, though its effects on AVFA remain largely unexplored. Additionally, capturing both intra-and inter-modal correlations through scalable representations is a crucial challenge in this field. To tackle these gaps, we introduce AVF-MAE++, a series audio-visual MAE designed to explore the impact of scaling on AVFA with a focus on advanced correlation modeling. Our method incorporates a novel audio-visual dual masking strategy and an improved modality encoder with a holistic view to better support scalable pre-training. Furthermore, we propose the Iteratively Audio-Visual Correlations Learning Module to improve correlations capture within the SSL framework, bridging the limitations of prior methods. To support smooth adaptation and mitigate overfitting, we also introduce a progressive semantics injection strategy, which structures training in three stages. Extensive experiments across 17 datasets, spanning three key AVFA tasks, demonstrate the superior performance of AVF-MAE++, establishing new state-of-the-art outcomes. Ablation studies provide further insights into the critical design choices driving these gains. Code is released at this URL.