In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge. ABAW 2023 aims to tackle the challenge of affective behavior analysis in natural contexts, with the ultimate goal of creating intelligent machines and robots that possess the ability to comprehend human emotions, feelings, and behaviors. For the Expression Classification Challenge, we propose a streamlined approach that handles the challenges of classification effectively. However, our main contribution lies in our use of diverse models and tools to extract multimodal features such as audio and video cues from the Hume-Reaction dataset. By studying, analyzing, and combining these features, we significantly enhance the model's accuracy for sentiment prediction in a multimodal context. Furthermore, our method achieves outstanding results on the Emotional Reaction Intensity (ERI) Estimation Challenge, surpassing the baseline method by an impressive 84\% increase, as measured by the Pearson Coefficient, on the validation dataset.

翻译：本文针对2023年野外情感行为分析挑战赛（ABAW）的两个子挑战——情绪反应强度估计挑战和表情分类挑战，提出了先进解决方案。ABAW 2023旨在解决自然情境下情感行为分析面临的挑战，最终目标是开发能够理解人类情绪、感受和行为的智能机器与机器人。针对表情分类挑战，我们提出了一种能够有效处理分类难题的精简方法。然而，我们的核心贡献在于采用多样化模型和工具，从Hume-Reaction数据集中提取音频与视频线索等多模态特征。通过研究、分析和融合这些特征，我们显著提升了模型在多模态情境下进行情感预测的准确率。此外，我们的方法在情绪反应强度估计挑战中取得了显著成果：在验证数据集上，以皮尔逊相关系数为衡量指标，该方法相较于基线方法提升了惊人的84%。