activity
20152018
most citedEnd-to-End Multimodal Emotion Recognition using Deep Neural Networks

755 citations · 1.4k across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2018

DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

Riza Alp Guler, Yuxiang Zhou, George Trigeorgis +4

In this work we use deep learning to establish dense correspondences between a 3D object model and an image "in the wild". We introduce "DenseReg", a fully-convolutional neural net…

cs.CV2017★ 32 cited

Joint Multi-view Face Alignment in the Wild

Jiankang Deng, George Trigeorgis, Yuxiang Zhou +1

The de facto algorithm for facial landmark estimation involves running a face detector with a subsequent deformable model fitting on the bounding box. This encompasses two basic pr…

cs.CV2017★ 755 cited

End-to-End Multimodal Emotion Recognition using Deep Neural Networks

Panagiotis Tzirakis, George Trigeorgis, Mihalis A. Nicolaou +2

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retr…

cs.CV2017★ 2 cited

3D Face Morphable Models "In-the-Wild"

James Booth, Epameinondas Antonakos, Stylianos Ploumpis +3

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single image…

cs.CV2016

DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

Rıza Alp Güler, George Trigeorgis, Epameinondas Antonakos +3

In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and t…

cs.CV2016★ 588 cited

Domain Separation Networks

Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman +2

The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach cir…

cs.CV2015★ 4 cited

A deep matrix factorization method for learning attribute representations

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou +1

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible tha…