Unsupervised Facial Landmark Detection

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 )

Unsupervised Facial Landmark Detection

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

Content

Unsupervised Learning of Object Landmarks through Conditional Image Generation

Self-supervised learning of a facial attribute embedding from video

Unsupervised Part-Based Disentangling of Object Shape and Appearance

Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings

SCOPS: Self-Supervised Co-Part Segmentation

Unsupervised Discovery of Object Landmarks as Structural Representations

Unsupervised Learning of Landmarks by Descriptor Vector Exchange

LatentKeypointGAN: Controlling GANs via Latent Keypoints

Deep Feature Factorization For Concept Discovery