PS-Net:high-frequency attention and Bayesian analysis based facial pore segmentation with no human annotationOA
Facial pore segmentation results can provide reliable evidence to simulate post-product pore conditions and provide product recommendations.However,accurately segmenting pores is challenging due to their small size,weak boundaries and dense distribution.It is also difficult to acquire precise annotation.Therefore,we formulate pore segmentation as a two-stage,weakly supervised task using both traditional and deep learning methods without human annotation.We propose a novel method called the pore segmentation network(PS-Net).Specifically,it contains pore feature extraction with coarse labels generated by a traditional method,as well as fine segmentation with progressively updated pseudo labels.Since pores provide high-frequency information about faces,we propose a high-frequency attention module that emphasizes low-level features.Moreover,we design a Bayesian module to identify pore shapes in high-level features.We establish a large-scale facial pore dataset with coarse labels that were generated via the difference of Gaussian(DoG)Pore method.PS-Net achieves the best performance on this dataset,proving its superiority compared with existing state-of-the-art segmentation methods.
Qing Zhang;Ling Li;Rizhao Cai;Qingli Li;Bandara Dissanayake;Yan Wang;Alex Kot
Shanghai Key Laboratory of Multidimensional Information Processing,East China Normal University,Shanghai,200241,China School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,639798,SingaporeSchool of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,639798,SingaporeSchool of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,639798,SingaporeShanghai Key Laboratory of Multidimensional Information Processing,East China Normal University,Shanghai,200241,ChinaBeauty Care R&D,Procter and Gamble,Mason,45040,OH,USAShanghai Key Laboratory of Multidimensional Information Processing,East China Normal University,Shanghai,200241,ChinaSchool of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,639798,Singapore
信息技术与安全科学
Bayesian networkCoarse annotationPore segmentationWeakly supervised segmentation
《Visual Intelligence》 2025 (1)
P.317-331,15
supported by the Fundamental Research Funds for the Central Universities(No.61975056)the National Natural Science Foundation of China(No.62101191)the Science and Technology Commission of Shanghai Municipality(Nos.20440713100,21ZR1420800,and 22DZ2229004).
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