arXiv 2209.154022025-12-03cs.CV

重新思考面部表情识别的学习范式

Rethinking the Learning Paradigm for Facial Expression Recognition

University of Trento \& Fondazione Bruno Kessler Trento Trento Italy；University of Trento Trento Trento Italy；University of Trento \& Fondazione Bruno Kessler；University of Trento

Weijie Wang, Bo Li, Nicu Sebe, Bruno Lepri

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AI总结：

本文提出使用弱监督策略训练面部表情识别模型，以应对模糊标注问题。

摘要：

由于主观众包标注和面部表情固有的类间相似性，现实中的面部表情识别（FER）数据集通常表现出模糊的标注。为了简化学习范式，大多数先前方法将模糊的标注结果转换为精确的一-hot标注，并以端到端监督的方式训练FER模型。在本文中，我们重新思考现有的训练范式，提出使用弱监督策略来训练FER模型，使用原始的模糊标注。

英文摘要：

Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-hot annotations and train FER models in an end-to-end supervised manner. In this paper, we rethink the existing training paradigm and propose that it is better to use weakly supervised strategies to train FER models with original ambiguous annotation.

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。