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Structural Dependence Learning Based on Self-attention for Face Alignment
Biying Li1,2; Zhiwei Liu1; Wei Zhou3; Haiyun Guo1; Xin Wen4; Min Huang2; Jinqiao Wang1,2
发表期刊Machine Intelligence Research
ISSN2731-538X
2024
卷号21期号:3页码:514-525
摘要Self-attention aggregates similar feature information to enhance the features. However, the attention covers nonface areas in face alignment, which may be disturbed in challenging cases, such as occlusions, and fails to predict landmarks. In addition, the learned feature similarity variance is not large enough in the experiment. To this end, we propose structural dependence learning based on self-attention for face alignment (SSFA). It limits the self-attention learning to the facial range and adaptively builds the significant landmark structure dependency. Compared with other state-of-the-art methods, SSFA effectively improves the performance on several standard facial landmark detection benchmarks and adapts more in challenging cases.
关键词Computer vision, face alignment, self-attention, facial structure, contextual information
DOI10.1007/s11633-023-1465-1
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文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/56479
专题学术期刊_Machine Intelligence Research
作者单位1.Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100083, China
3.Alpha (Beijing) Private Equity, Beijing 100083, China
4.School of Computer Science, National University of Defense Technology, Changsha 410073, China
第一作者单位中国科学院自动化研究所
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Biying Li,Zhiwei Liu,Wei Zhou,et al. Structural Dependence Learning Based on Self-attention for Face Alignment[J]. Machine Intelligence Research,2024,21(3):514-525.
APA Biying Li.,Zhiwei Liu.,Wei Zhou.,Haiyun Guo.,Xin Wen.,...&Jinqiao Wang.(2024).Structural Dependence Learning Based on Self-attention for Face Alignment.Machine Intelligence Research,21(3),514-525.
MLA Biying Li,et al."Structural Dependence Learning Based on Self-attention for Face Alignment".Machine Intelligence Research 21.3(2024):514-525.
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