IEICE TRANSACTIONS on Information
Dynamic Attentive Convolution for Facial Beauty Prediction
Summary :
Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.2 pp.239-243
- Publication Date
- 2024/02/01
- Publicized
- 2023/11/07
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDL8058
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Zhishu SUN
Fuzhou University
Zilong XIAO
Fuzhou University
Yuanlong YU
Fuzhou University
Luojun LIN
Fuzhou University
Keyword
Latest Issue
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Zhishu SUN, Zilong XIAO, Yuanlong YU, Luojun LIN, "Dynamic Attentive Convolution for Facial Beauty Prediction" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 2, pp. 239-243, February 2024, doi: 10.1587/transinf.2023EDL8058.
Abstract: Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8058/_p
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@ARTICLE{e107-d_2_239,
author={Zhishu SUN, Zilong XIAO, Yuanlong YU, Luojun LIN, },
journal={IEICE TRANSACTIONS on Information},
title={Dynamic Attentive Convolution for Facial Beauty Prediction},
year={2024},
volume={E107-D},
number={2},
pages={239-243},
abstract={Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.},
keywords={},
doi={10.1587/transinf.2023EDL8058},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Dynamic Attentive Convolution for Facial Beauty Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 239
EP - 243
AU - Zhishu SUN
AU - Zilong XIAO
AU - Yuanlong YU
AU - Luojun LIN
PY - 2024
DO - 10.1587/transinf.2023EDL8058
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2024
AB - Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
ER -
English
