Papers Unsupervised Facial Landmark Detection
“Unsupervised Facial Landmark Detection” 태그가 달린 논문 15편 · 필터 해제
Unsupervised Image Representation Learning with Deep Latent Particles
We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', whe…
Image ManipulationModel SelectionRepresentation LearningUnsupervised Facial Landmark Detection+1AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
Structured representations such as keypoints are widely used in pose transfer, conditional image generation, animation, and 3D reconstruction. However, their supervised learning requires expensive annotation for each tar…
Pose EstimationSelf-Supervised LearningUnsupervised Facial Landmark DetectionUnsupervised Facial Landmark Detection on MAFL+4GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods ex…
Image AugmentationImage GenerationSegmentationUnsupervised Facial Landmark Detection+2Unsupervised Part Segmentation through Disentangling Appearance and Shape
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognitio…
DisentanglementObjectSegmentationSemantic Segmentation+1LatentKeypointGAN: Controlling Images via Latent Keypoints
Generative adversarial networks (GANs) have attained photo-realistic quality in image generation. However, how to best control the image content remains an open challenge. We introduce LatentKeypointGAN, a two-stage GAN …
Image GenerationImage Quality AssessmentKeypoint DetectionUnsupervised Facial Landmark Detection+2Unsupervised 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. However, this method does not explicitly gu…
Facial Landmark DetectionObjectUnsupervised Facial Landmark DetectionSCOPS: 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. Existing works on part segmentation is dominated by supervised approaches that rely o…
ObjectSegmentationUnsupervised Facial Landmark DetectionUnsupervised Human Pose Estimation+2Unsupervised Part-Based Disentangling of Object Shape and Appearance
Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to dise…
Appearance TransferImage GenerationObjectPose Prediction+3Self-supervised learning of a facial attribute embedding from video
We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we introduce a network, Facial Attributes-Ne…
AttributeSelf-Supervised LearningUnsupervised Facial Landmark DetectionDeep Feature Factorization For Concept Discovery
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned feat…
Unsupervised Facial Landmark DetectionUnsupervised Human Pose EstimationUnsupervised KeypointsUnsupervised Learning of Object Landmarks through Conditional Image Generation
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. We cast this as the problem of generating images that combine the appearanc…
Conditional Image GenerationImage GenerationObjectUnsupervised Facial Landmark DetectionDeforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template paradigm, shape is represented as a deforma…
Unsupervised Facial Landmark DetectionUnsupervised Discovery of Object Landmarks as Structural Representations
Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way. This paper addresses the problem of learning obj…
ObjectUnsupervised Facial Landmark DetectionUnsupervised Human Pose EstimationUnsupervised Keypoint EstimationUnsupervised learning of object frames by dense equivariant image labelling
One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, moti…
ObjectOptical Flow EstimationUnsupervised Facial Landmark DetectionUnsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object cate…
ObjectUnsupervised Facial Landmark DetectionUnsupervised Human Pose EstimationUnsupervised Keypoints