TransBLS: transformer combined with broad learning system for facial beauty prediction

Gan, Junying; Xie, Xiaoshan; He, Guohui; Luo, Heng

doi:10.1007/s10489-023-04931-8

Cited by 1 publication

(3 citation statements)

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“…In [4], a network named E-BLS fusing EffeicientNet and a broad learning system was applied in FBP. In [5], a tiny network named TransBLS-T fusing transformer and broad learning system was proposed to improve FBP. The performance of the proposed method surpasses that of the other method.…”

Section: Comparison Experiments Between the Proposed Methods And Othe...mentioning

confidence: 99%

“…Solving the aforementioned issue has become a popular subject in the field of FBP research. At present, some progress has been made in FBP research [1][2][3][4][5][6][7]. In [1], a novel personalized FBP approach based on meta-learning was designed to apply in some small databases.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], an innovative method of broad learning fused with transfer learning was applied in FBP, which received better performance in prediction accuracy and training speed. In [5], an adaptive transformer with global and local multihead self-attention was proposed for FBP, which achieved better performance Electronics 2024, 13, 179 2 of 17 on several datasets of different scales. In [6], a dynamic convolution vision transformer named FBPFormer was proposed which aims to focus on both local and global facial beauty features.…”

Section: Introductionmentioning

confidence: 99%

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Facial Beauty Prediction Combined with Multi-Task Learning of Adaptive Sharing Policy and Attentional Feature Fusion

Gan,

Luo,

Xiong

et al. 2023

Electronics

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Facial beauty prediction (FBP) is a leading research subject in the field of artificial intelligence (AI), in which computers make facial beauty judgments and predictions similar to those of humans. At present, the methods are mainly based on deep neural networks. However, there still exist some problems such as insufficient label information and overfitting. Multi-task learning uses label information from multiple databases, which increases the utilization of label information and enhances the feature extraction ability of the network. Attentional feature fusion (AFF) combines semantic information and introduces an attention mechanism to reduce the risk of overfitting. In this study, the multi-task learning of an adaptive sharing policy combined with AFF is presented based on the adaptive sharing (AdaShare) network in FBP. First, an adaptive sharing policy is added to multi-task learning with ResNet18 as the backbone network. Second, the AFF is introduced at the short skip connections of the network. The proposed method improves the accuracy of FBP by solving the problems of insufficient label information and overfitting issues. The experimental results based on the large-scale Asia facial beauty database (LSAFBD) and SCUT-FBP5500 databases show that the proposed method outperforms the single-database single-task baseline and can be applied extensively in image classification and other fields.

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Section: Comparison Experiments Between the Proposed Methods And Othe...mentioning

confidence: 99%