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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.00603 |
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| _version_ | 1866916377214844928 |
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| author | Liang, Xuefeng Liu, Zhenyou Lin, Jian Yang, Xiaohui Kumada, Takatsune |
| author_facet | Liang, Xuefeng Liu, Zhenyou Lin, Jian Yang, Xiaohui Kumada, Takatsune |
| contents | Previous Facial Beauty Prediction (FBP) methods generally model FB feature of an image as a point on the latent space, and learn a mapping from the point to a precise score. Although existing regression methods perform well on a single dataset, they are inclined to be sensitive to test data and have weak generalization ability. We think they underestimate two inconsistencies existing in the FBP problem: 1. inconsistency of FB standards among multiple datasets, and 2. inconsistency of human cognition on FB of an image. To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition. The key contribution of UOL is a designed distribution comparison module, which enables conventional order learning to learn the order of uncertain data. Extensive experiments on five datasets show that UOL outperforms the state-of-the-art methods on both accuracy and generalization ability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty-oriented Order Learning for Facial Beauty Prediction Liang, Xuefeng Liu, Zhenyou Lin, Jian Yang, Xiaohui Kumada, Takatsune Computer Vision and Pattern Recognition Previous Facial Beauty Prediction (FBP) methods generally model FB feature of an image as a point on the latent space, and learn a mapping from the point to a precise score. Although existing regression methods perform well on a single dataset, they are inclined to be sensitive to test data and have weak generalization ability. We think they underestimate two inconsistencies existing in the FBP problem: 1. inconsistency of FB standards among multiple datasets, and 2. inconsistency of human cognition on FB of an image. To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition. The key contribution of UOL is a designed distribution comparison module, which enables conventional order learning to learn the order of uncertain data. Extensive experiments on five datasets show that UOL outperforms the state-of-the-art methods on both accuracy and generalization ability. |
| title | Uncertainty-oriented Order Learning for Facial Beauty Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.00603 |