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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Most implemented papers
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
xingzhehe/ganseg
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CVPR 2022
Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing.
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
Our key ingredients are i) an encoder that predicts keypoint locations in an input image, ii) a shared graph as a latent variable that links the same pairs of keypoints in every image, iii) an intermediate edge map that combines the latent graph edge weights and keypoint locations in a soft, differentiable manner, and iv) an inpainting objective on randomly masked images.
Unsupervised Image Representation Learning with Deep Latent Particles
taldatech/deep-latent-particles-pytorch
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We propose a new representation of visual data that disentangles object position from appearance.
