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Unsupervised Facial Landmark Detection
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Facial landmark detection in the unsupervised setting popularized by [1]. The evaluation occurs in two stages: (1) Embeddings are first learned in an unsupervised manner (i.e. without labels); (2) A simple regressor is trained to regress landmarks from the unsupervised embedding.
[1] Thewlis, James, Hakan Bilen, and Andrea Vedaldi. "Unsupervised learning of object landmarks by factorized spatial embeddings." Proceedings of the IEEE International Conference on Computer Vision. 2017.
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised learning of object landmarks by factorized spatial embeddings )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
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Unsupervised Part-Based Disentangling of Object Shape and Appearance
Self-supervised learning of a facial attribute embedding from video
Deep Feature Factorization For Concept Discovery
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
Unsupervised Discovery of Object Landmarks as Structural Representations
Unsupervised learning of object frames by dense equivariant image labelling
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning Deep Representation for Face Alignment with Auxiliary Attributes
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