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
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Most implemented papers
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
zhixinshu/DeformingAutoencoders-pytorch
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ECCV 2018
In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner.
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.
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.
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.
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.
Unsupervised learning of object landmarks by factorized spatial embeddings
alldbi/Factorized-Spatial-Embeddings
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ICCV 2017
Learning automatically the structure of object categories remains an important open problem in computer vision.
Unsupervised Discovery of Object Landmarks as Structural Representations
YutingZhang/lmdis-rep
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CVPR 2018
Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way.
SCOPS: Self-Supervised Co-Part Segmentation
Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations.
Unsupervised Learning of Landmarks by Descriptor Vector Exchange
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.
LatentKeypointGAN: Controlling GANs via Latent Keypoints
Generative adversarial networks (GANs) have attained photo-realistic quality in image generation.
