50 citations · 64 across the 8 of their papers we have counts for
11 papers
CoViG: Coarsened Vision GNN for Image Classification
Martial Guidez, Stefan Duffner, Christophe Garcia
Although Graph Neural Networks are widely used in various domains, they remained mostly unexplored in computer vision. Convolutional Neural Networks and Vision Transformers were le…
ERDE: Entropy-Regularized Distillation for Early-exit
Martial Guidez, Stefan Duffner, Yannick Alpou +2
Although deep neural networks and in particular Convolutional Neural Networks have demonstrated state-of-the-art performance in image classification with relatively high efficiency…
Long-Range Transformer Architectures for Document Understanding
Thibault Douzon, Stefan Duffner, Christophe Garcia +1
Since their release, Transformers have revolutionized many fields from Natural Language Understanding to Computer Vision. Document Understanding (DU) was not left behind with first…
Improving Information Extraction on Business Documents with Specific Pre-Training Tasks
Thibault Douzon, Stefan Duffner, Christophe Garcia +1
Transformer-based Language Models are widely used in Natural Language Processing related tasks. Thanks to their pre-training, they have been successfully adapted to Information Ext…
Low-complexity Approximate Convolutional Neural Networks
R. J. Cintra, S. Duffner, C. Garcia +1
In this paper, we present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet). The idea is to approximate all elements of a g…
Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm
Anthony Berthelier, Yongzhe Yan, Thierry Chateau +3
While deep neural networks (DNNs) have proven to be efficient for numerous tasks, they come at a high memory and computation cost, thus making them impractical on resource-limited…