Unsupervised Facial Landmark Detection
13 papers with code • 6 benchmarks • 3 datasets
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.
( Image credit: Unsupervised learning of object landmarks by factorized spatial embeddings )
Benchmarks
These leaderboards are used to track progress in Unsupervised Facial Landmark Detection
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300W
AFLW
MAFL
Latest papers with no code
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