4k citations · 8.4k across the 17 of their papers we have counts for
13 papers · 1 filter
Specularity Factorization for Low-Light Enhancement
Saurabh Saini, P J Narayanan
We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modula…
ResNet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, Hervé Jégou
The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture…
XCiT: Cross-Covariance Image Transformers
Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron +8
Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yiel…
ResMLP: Feedforward networks for image classification with data-efficient training
Hugo Touvron, Piotr Bojanowski, Mathilde Caron +8
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which…
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron, Hugo Touvron, Ishan Misra +4
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the…
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
Ben Graham, Alaaeldin El-Nouby, Hugo Touvron +4
We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. Our work exploits recent findings in at…
Going deeper with Image Transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles +2
Transformers have been recently adapted for large scale image classification, achieving high scores shaking up the long supremacy of convolutional neural networks. However the opti…
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt +3
Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially l…
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze +3
Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained…
Grafit: Learning fine-grained image representations with coarse labels
Hugo Touvron, Alexandre Sablayrolles, Matthijs Douze +2
This paper tackles the problem of learning a finer representation than the one provided by training labels. This enables fine-grained category retrieval of images in a collection a…
Powers of layers for image-to-image translation
Hugo Touvron, Matthijs Douze, Matthieu Cord +1
We propose a simple architecture to address unpaired image-to-image translation tasks: style or class transfer, denoising, deblurring, deblocking, etc. We start from an image autoe…
Fixing the train-test resolution discrepancy: FixEfficientNet
Hugo Touvron, Andrea Vedaldi, Matthijs Douze +1
This paper provides an extensive analysis of the performance of the EfficientNet image classifiers with several recent training procedures, in particular one that corrects the disc…
Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze +1
Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between th…