HSEmotion团队在ABAW-10竞赛中的表现：面部表情识别、情绪估值估计、动作单元检测和细粒度暴力分类
HSEmotion Team at ABAW-10 Competition: Facial Expression Recognition, Valence-Arousal Estimation, Action Unit Detection and Fine-Grained Violence Classification
AI总结：
本文提出了一种基于预训练EfficientNet模型的快速方法，用于面部情绪理解任务，同时通过滑动窗口平滑噪声以提升预测精度，并在细粒度暴力检测任务中评估了多种预训练架构。
摘要：
本文报告了我们在第10届情感行为分析在野外（ABAW）竞赛中的结果。对于帧级面部情绪理解任务（帧级面部表情识别、情感估值估计、动作单元检测），我们提出了一种基于预训练EfficientNet模型的快速方法。如果后者的置信度超过阈值，则使用其预测结果。否则，我们将嵌入向量输入到在AffWild2数据集上训练的简单多层感知机中。估计的类别级分数通过固定大小的滑动窗口平滑以减轻帧级预测中的噪声。对于细粒度暴力检测任务，我们检查了几种预训练架构用于帧嵌入及其在视频分类中的聚合。在ABAW挑战赛的四个任务上的实验结果表明，我们的方法在验证指标上显著优于现有基线。
英文摘要：
This article presents our results for the 10th Affective Behavior Analysis in-the-Wild (ABAW) competition. For frame-wise facial emotion understanding tasks (frame-wise facial expression recognition, valence-arousal estimation, action unit detection), we propose a fast approach based on facial embedding extraction with pre-trained EfficientNet-based emotion recognition models. If the latter model's confidence exceeds a threshold, its prediction is used. Otherwise, we feed embeddings into a simple multi-layered perceptron trained on the AffWild2 dataset. Estimated class-level scores are smoothed in a sliding window of fixed size to mitigate noise in frame-wise predictions. For the fine-grained violence detection task, we examine several pre-trained architectures for frame embeddings and their aggregation for video classification. Experimental results on four tasks from the ABAW challenge demonstrate that our approach significantly improves validation metrics over 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。
