Rethinking the Learning Paradigm for Facial Expression Recognition
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917120069074944 |
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| author | Wang, Weijie Li, Bo Sebe, Nicu Lepri, Bruno |
| author_facet | Wang, Weijie Li, Bo Sebe, Nicu Lepri, Bruno |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_15402 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Rethinking the Learning Paradigm for Facial Expression Recognition Wang, Weijie Li, Bo Sebe, Nicu Lepri, Bruno Computer Vision and Pattern Recognition 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. |
| title | Rethinking the Learning Paradigm for Facial Expression Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2209.15402 |