Affective Behavior Analysis aims to facilitate technology emotionally smart,
creating a world where devices can understand and react to our emotions as
humans do. To comprehensively evaluate the authenticity and applicability of
emotional behavior analysis techniques in natural environments, the 6th
competition on Affective Behavior Analysis in-the-wild (ABAW) utilizes the
Aff-Wild2, Hume-Vidmimic2, and C-EXPR-DB datasets to set up five competitive
tracks, i.e., Valence-Arousal (VA) Estimation, Expression (EXPR) Recognition,
Action Unit (AU) Detection, Compound Expression (CE) Recognition, and Emotional
Mimicry Intensity (EMI) Estimation. In this paper, we present our method
designs for the five tasks. Specifically, our design mainly includes three
aspects: 1) Utilizing a transformer-based feature fusion module to fully
integrate emotional information provided by audio signals, visual images, and
transcripts, offering high-quality expression features for the downstream
tasks. 2) To achieve high-quality facial feature representations, we employ
Masked-Auto Encoder as the visual features extraction model and fine-tune it
with our facial dataset. 3) Considering the complexity of the video collection
scenes, we conduct a more detailed dataset division based on scene
characteristics and train the classifier for each scene. Extensive experiments
demonstrate the superiority of our designs.

通过利用各种数据集和方法设计，我们可以在自然环境中全面评估情感行为分析技术的真实性和适用性，从而实现情感智能技术，使设备能像人类一样理解和反应我们的情感。