实验结果表明,DFER-CLIP在多个指标上均优于现有的DFER方法。具体来说,在DFEW数据集上,DFER-CLIP的用户平均正确率(UAR)和加权平均正确率(WAR)分别提高了2.05%和0.41%;在FERV39k数据集上,这两个指标分别提高了0.04%和0.31%;在MAFW数据集上,则分别提高了4.09%和4.37%。这些结果充分证明了DFER-CLIP在动态面部表...
DFER-CLIP This is a PyTorch implementation of the paper: Zengqun Zhao, Ioannis Patras. "Prompting Visual-Language Models for Dynamic Facial Expression Recognition", British Machine Vision Conference (BMVC), 2023. Overview Requirement The code is built with following libraries: pytorch scikit-learn ...
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DFER-CLIP是一种结合了动态面部特征和与表情相关的文字描述的面部表情识别方法。它利用CLIP(Contrastive Language-Image Pre-training)模型的强大能力,通过对比学习的方式训练模型,使其能够学习到图像和文本之间的对应关系。DFER-CLIP在动态面部表情识别任务中取得了显著进展,为更自然、更真实的表情识别提供了可能。 技术...