Rethinking the Learning Paradigm for Facial Expression Recognition

• URL: http://arxiv.org/abs/2209.15402v2
• Date: Tue, 3 Sep 2024 09:37:16 GMT
• Title: Rethinking the Learning Paradigm for Facial Expression Recognition
• Authors: Weijie Wang, Nicu Sebe, Bruno Lepri,
• Abstract summary: 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. This paper argues that it is better to use weakly supervised strategies to train FER models with original ambiguous annotation.
• Score: 56.050738381526116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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