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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2015, Volume 7-12-, pp. 3659 - 3667
Cascaded regression approaches have been recently shown to achieve state-of-the-art performance for many computer vision tasks. Beyond its connection to... 
Jacobian matrices | Deformable models | Training | Shape | Computational modeling | Face | Optimization | Computer vision | Alignment | Facial | Regression | Pattern recognition | Descent | Jacobians
Conference Proceeding
2013 IEEE International Conference on Computer Vision, ISSN 1550-5499, 12/2013, pp. 593 - 600
We describe a very simple framework for deriving the most-well known optimization problems in Active Appearance Models (AAMs), and most importantly for... 
Jacobian matrices | Shape | Silicon carbide | Computational modeling | Face alignment | In-the-wild | Active Appearance Models | AAMs | Robustness | Active appearance model | Optimization | Training | Computer vision | Fittings | Algorithms | Conferences | Computation | Mathematical models | Matlab
Conference Proceeding
2014 IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 06/2014, pp. 1851 - 1858
Arguably, Deformable Part Models (DPMs) are one of the most prominent approaches for face alignment with impressive results being recently reported for both... 
Deformable models | Training | Deformable Part Models | Shape | Computational modeling | In-the-wild | Face Alignment | Robustness | Face | Optimization | Gauss-Newton | Computer vision | Fittings | Alignment | Deformation | Cost function | Mathematical models | Formability | Pattern recognition
Conference Proceeding
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 10/2010, Volume 32, Issue 10, pp. 1899 - 1906
Journal Article
Computer Vision and Image Understanding, ISSN 1077-3142, 02/2017, Volume 155, pp. 70 - 82
We present a novel frequency-domain image registration technique, which employs histograms of oriented gradients providing subpixel estimates. Our method... 
Registration in frequency domain | Histogram of oriented gradients | Phase correlation | Fourier | Subpixel
Journal Article
International journal of computer vision, ISSN 0920-5691, 09/2016, Volume 122, Issue 1, pp. 17 - 33
Fitting algorithms for Active Appearance Models (AAMs) are usually considered to be robust but slow or fast but less able to generalize well to unseen... 
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2016, Volume 9911, pp. 717 - 732
This paper is on human pose estimation using Convolutional Neural Networks. Our main contribution is a CNN cascaded architecture specifically designed for... 
Part heatmap regression | Human pose estimation | Convolutional Neural Networks | Computer Science - Computer Vision and Pattern Recognition
Conference Proceeding
International Journal of Computer Vision, ISSN 0920-5691, 3/2017, Volume 122, Issue 1, pp. 17 - 33
Journal Article
2017 IEEE International Conference on Computer Vision (ICCV), ISSN 1550-5499, 10/2017, Volume 2017-, pp. 1021 - 1030
This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To... 
Training | Computer vision | Three-dimensional displays | Two dimensional displays | Face | Testing | Videos
Conference Proceeding
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 8/2018, pp. 1 - 1
Our goal is to design architectures that retain the groundbreaking performance of Convolutional Neural Networks (CNNs) for landmark localization and at the... 
Training | Quantization (signal) | Human pose estimation | Pose estimation | Neural networks | Face alignment | Computer architecture | Binary Convolutional Neural Networks | Landmark localization | Residual learning | Face | Task analysis
Journal Article
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 12/2018, pp. 109 - 117
Conference Proceeding
Image and Vision Computing, ISSN 0262-8856, 03/2016, Volume 47, pp. 3 - 18
Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the... 
Facial landmark localization | Semi-automatic annotation tool | Facial database | Challenge | COMPUTER SCIENCE, SOFTWARE ENGINEERING | PICTORIAL STRUCTURES | ALIGNMENT | MODELS | COMPUTER SCIENCE, THEORY & METHODS | OPTICS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 04/2018, Volume 2018-, pp. 4974 - 4978
In this paper we present a deep learning architecture for extracting word embeddings for visual speech recognition. The embeddings summarize the information of... 
Training | Visualization | Lipreading | Error analysis | Biological system modeling | Speech recognition | Machine learning | Computer architecture | Low-shot Learning | Visual Speech Recognition | Deep Learning | Word Embeddings
Conference Proceeding
IEEE Transactions on Image Processing, ISSN 1057-7149, 02/2017, Volume 26, Issue 2, pp. 1040 - 1053
Journal Article
2017 IEEE International Conference on Computer Vision (ICCV), ISSN 1550-5499, 10/2017, Volume 2017-, pp. 3726 - 3734
Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact... 
Training | Computer vision | Quantization (signal) | Face | Pose estimation | Computer architecture
Conference Proceeding
2017 IEEE International Conference on Computer Vision (ICCV), ISSN 1550-5499, 10/2017, Volume 2017-, pp. 1031 - 1039
3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial... 
Geometry | Three-dimensional displays | Shape | Two dimensional displays | Face | Image reconstruction | Optimization
Conference Proceeding
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