HSEmotion 团队参加 ABAW-8 竞赛：视听矛盾/犹豫、情绪模仿强度与面部表情识别
HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition
AI总结：
本文提出 HSEmotion 团队在 ABAW-8 中的方案，融合 EmotiEffLib 面部描述符、声学与语音文本特征，利用帧选择和 MLP 完成三类情感识别任务并显著优于基线。
摘要：
本文介绍了我们在第八届野外情感行为分析（ABAW）竞赛中的结果。我们将由预训练模型（即我们的 EmotiEffLib 库）提取的面部情绪描述符，与声学特征以及从语音中识别出的文本嵌入相结合。帧级特征经过聚合后输入简单分类器，例如多层感知机（含一个隐藏层的前馈神经网络），以预测矛盾/犹豫和面部表情。在面部表情任务中，我们还使用预训练的面部表情识别模型来选择高分视频帧，并避免由特定领域的视频分类器处理这些帧。情绪模仿强度的视频级预测通过简单聚合帧级特征并训练多层感知机来实现。针对 ABAW 挑战中三项任务的实验结果表明，与现有基线相比，我们的方法显著提高了验证指标。
英文摘要：
This article presents our results for the eighth Affective Behavior Analysis in-the-Wild (ABAW) competition. We combine facial emotional descriptors extracted by pre-trained models, namely, our EmotiEffLib library, with acoustic features and embeddings of texts recognized from speech. The frame-level features are aggregated and fed into simple classifiers, e.g., multi-layered perceptron (feed-forward neural network with one hidden layer), to predict ambivalence/hesitancy and facial expressions. In the latter case, we also use the pre-trained facial expression recognition model to select high-score video frames and prevent their processing with a domain-specific video classifier. The video-level prediction of emotional mimicry intensity is implemented by simply aggregating frame-level features and training a multi-layered perceptron. Experimental results for three tasks from the ABAW challenge demonstrate that our approach significantly increases validation metrics compared to existing baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
