This paper presents our submission to the Expression Classification Challenge of the fifth Affective Behavior Analysis in-the-wild (ABAW) Competition. In our method, multimodal feature combinations extracted by several different pre-trained models are applied to capture more effective emotional information. For these combinations of visual and audio modal features, we utilize two temporal encoders to explore the temporal contextual information in the data. In addition, we employ several ensemble strategies for different experimental settings to obtain the most accurate expression recognition results. Our system achieves the average F1 Score of 0.45774 on the validation set.

翻译：本文介绍了我们在第五届野外情感行为分析（ABAW）竞赛的表情分类挑战中的提交方案。在我们的方法中，采用多个不同预训练模型提取的多模态特征组合，以捕获更有效的情感信息。针对这些视觉与音频模态特征的组合，我们利用两个时序编码器来探索数据中的时间上下文信息。此外，针对不同的实验设置，我们采用多种集成策略以获取最准确的表情识别结果。我们的系统在验证集上实现了平均F1得分为0.45774。