4 papers
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety
Nathan Hubens, Matei Mancas, Bernard Gosselin +2
Neural network pruning is a widely used strategy for reducing model storage and computing requirements. It allows to lower the complexity of the network by introducing sparsity in…
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models
Antoine Maiorca, Nathan Hubens, Sohaib Laraba +1
In the past few years, neural character animation has emerged and offered an automatic method for animating virtual characters. Their motion is synthesized by a neural network. Con…
Where Is My Mind (looking at)? Predicting Visual Attention from Brain Activity
Victor Delvigne, Noé Tits, Luca La Fisca +5
Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, artificial intelligence and medicine. One of the most common…
Modulated Self-attention Convolutional Network for VQA
Jean-Benoit Delbrouck, Antoine Maiorca, Nathan Hubens +1
As new data-sets for real-world visual reasoning and compositional question answering are emerging, it might be needed to use the visual feature extraction as a end-to-end process…