As emotions play a central role in human communication, automatic emotion
recognition has attracted increasing attention in the last two decades. While
multimodal systems enjoy high performances on lab-controlled data, they are
still far from providing ecological validity on non-lab-controlled, namely
'in-the-wild' data. This work investigates audiovisual deep learning approaches
for emotion recognition in-the-wild problem. We particularly explore the
effectiveness of architectures based on fine-tuned Convolutional Neural
Networks (CNN) and Public Dimensional Emotion Model (PDEM), for video and audio
modality, respectively. We compare alternative temporal modeling and fusion
strategies using the embeddings from these multi-stage trained
modality-specific Deep Neural Networks (DNN). We report results on the AffWild2
dataset under Affective Behavior Analysis in-the-Wild 2024 (ABAW'24) challenge
protocol.

通过调查音频和视觉深度学习方法，为非控制环境中的情绪识别问题提供有效的体系结构。使用基于微调的卷积神经网络（CNN）和公共维度情绪模型（PDEM）进行视频和音频模态的比较，并使用这些多阶段训练的模态特定的深度神经网络（DNN）的嵌入来比较替代的时间建模和融合策略。在 ABAW'24 挑战协议下，对 AffWild2 数据集进行了结果报告。