244 citations · 1.3k across the 42 of their papers we have counts for
8 papers · 1 filter
Speech-driven facial animation using polynomial fusion of features
Triantafyllos Kefalas, Konstantinos Vougioukas, Yannis Panagakis +3
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting…
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation
Jean Kossaifi, Antoine Toisoul, Adrian Bulat +3
Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters…
Matrix and tensor decompositions for training binary neural networks
Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos +1
This paper is on improving the training of binary neural networks in which both activations and weights are binary. While prior methods for neural network binarization binarize eac…
Improved training of binary networks for human pose estimation and image recognition
Adrian Bulat, Georgios Tzimiropoulos, Jean Kossaifi +1
Big neural networks trained on large datasets have advanced the state-of-the-art for a large variety of challenging problems, improving performance by a large margin. However, unde…
T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor
Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos +1
Recent findings indicate that over-parametrization, while crucial for successfully training deep neural networks, also introduces large amounts of redundancy. Tensor methods have t…
Incremental multi-domain learning with network latent tensor factorization
Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos +1
The prominence of deep learning, large amount of annotated data and increasingly powerful hardware made it possible to reach remarkable performance for supervised classification ta…
Tensor Dropout for Robust Learning
Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis +4
CNNs achieve remarkable performance by leveraging deep, over-parametrized architectures, trained on large datasets. However, they have limited generalization ability to data outsid…
SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
Jean Kossaifi, Robert Walecki, Yannis Panagakis +10
Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices…