30 citations · 61 across the 5 of their papers we have counts for
5 papers · 1 filter
Co-training Submodels for Visual Recognition
Hugo Touvron, Matthieu Cord, Maxime Oquab +3
We introduce submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth. Given a neural network to be trained, for each sample we…
DeiT III: Revenge of the ViT
Hugo Touvron, Matthieu Cord, Hervé Jégou
A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. It has limited built-in architectural priors, in contrast to more recent…
Three things everyone should know about Vision Transformers
Hugo Touvron, Matthieu Cord, Alaaeldin El-Nouby +2
After their initial success in natural language processing, transformer architectures have rapidly gained traction in computer vision, providing state-of-the-art results for tasks…
Augmenting Convolutional networks with attention-based aggregation
Hugo Touvron, Matthieu Cord, Alaaeldin El-Nouby +4
We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning. We replace the final average pooling by an attention-based aggre…
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?
Alaaeldin El-Nouby, Gautier Izacard, Hugo Touvron +3
Pre-training models on large scale datasets, like ImageNet, is a standard practice in computer vision. This paradigm is especially effective for tasks with small training sets, for…