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LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-Annotation Face Alignment

ICLR 2022(2022)

Univ British Columbia | Univ Queensland | PhD student | Southern Univ Sci & Technol

Cited 1|Views17
Abstract
We innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks. Due to the embedding of LDDMM into a deep network, LDDMM-Face can consistently annotate facial landmarks without ambiguity and flexibly handle various annotation schemes, and can even predict dense annotations from sparse ones. Our method can be easily integrated into various face alignment networks. We extensively evaluate LDDMM-Face on four benchmark datasets: 300W, WFLW, HELEN and COFW-68. LDDMM-Face is comparable or superior to state-of-the-art methods for traditional within-dataset and same-annotation settings, but truly distinguishes itself with outstanding performance when dealing with weakly-supervised learning (partial-to-full), challenging cases (e.g., occluded faces), and different training and prediction datasets. In addition, LDDMM-Face shows promising results on the most challenging task of predicting across datasets with different annotation schemes.
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Key words
Face alignment,Facial landmarks,Diffeomorphic mapping,Deep learning
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要点】:本研究通过PET/CT技术探讨了钙化和炎症在主动脉狭窄和动脉粥样硬化疾病进展中的作用,发现NaF对于预测主动脉狭窄的进展和识别易损斑块具有潜在价值。

方法】:研究分为两部分,第一部分采用PET技术,通过NaF和FDG的摄取评估了101名主动脉狭窄患者和20名正常瓣膜对照者的瓣膜钙化和炎症情况;第二部分评估了106名稳定型冠心病患者和15名心肌梗死后患者的冠状动脉中NaF和FDG的活性。

实验】:实验通过比较患者和对照的tracer activity (TBR),发现主动脉狭窄患者的NaF和FDG摄取高于正常对照,且NaF摄取与瓣膜严重程度呈正相关,与碱性磷酸酶染色有高度相关性,是疾病进展的更好预测因子。在稳定型冠心病患者中,增加的NaF活性与不良心血管事件的高发生率相关;在心肌梗死后患者中,NaF在罪犯病变部位的摄取显著增加。数据集名称未在摘要中提及。