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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Latest papers
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.
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.
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.
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Generative adversarial networks (GANs) have attained photo-realistic quality in image generation.
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Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.
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Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations.
Unsupervised Part-Based Disentangling of Object Shape and Appearance
NVIDIA/UnsupervisedLandmarkLearning
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CVPR 2019
Large intra-class variation is the result of changes in multiple object characteristics.
Self-supervised learning of a facial attribute embedding from video
oawiles/FAb-Net
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We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time.
Deep Feature Factorization For Concept Discovery
jacobgil/pytorch-grad-cam
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ECCV 2018
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images.
Unsupervised Learning of Object Landmarks through Conditional Image Generation
tomasjakab/imm
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NeurIPS 2018
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision.
