1.4k citations · 2.6k across the 79 of their papers we have counts for
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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…
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang +3
Foundation models are redefining how AI systems are built. Practitioners now follow a standard procedure to build their machine learning solutions: from a pre-trained foundation mo…
Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval
Mustafa Shukor, Nicolas Thome, Matthieu Cord
Vision-Language Pretraining (VLP) and Foundation models have been the go-to recipe for achieving SoTA performance on general benchmarks. However, leveraging these powerful techniqu…
CoMFormer: Continual Learning in Semantic and Panoptic Segmentation
Fabio Cermelli, Matthieu Cord, Arthur Douillard
Continual learning for segmentation has recently seen increasing interest. However, all previous works focus on narrow semantic segmentation and disregard panoptic segmentation, an…
OCTET: Object-aware Counterfactual Explanations
Mehdi Zemni, Mickaël Chen, Éloi Zablocki +3
Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern.…
DiffEdit: Diffusion-based semantic image editing with mask guidance
Guillaume Couairon, Jakob Verbeek, Holger Schwenk +1
Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we prop…
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
Mustafa Shukor, Guillaume Couairon, Matthieu Cord
Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks. The current trend is to move towards ever larger models and pretraining…
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks. However it often comes at a computational price which…
LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation
Florent Bartoccioni, Éloi Zablocki, Andrei Bursuc +3
Recent works in autonomous driving have widely adopted the bird's-eye-view (BEV) semantic map as an intermediate representation of the world. Online prediction of these BEV maps in…
Swapping Semantic Contents for Mixing Images
Rémy Sun, Clément Masson, Gilles Hénaff +2
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bot…
Towards efficient feature sharing in MIMO architectures
Rémy Sun, Alexandre Ramé, Clément Masson +2
Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free…
Dynamic Query Selection for Fast Visual Perceiver
Corentin Dancette, Matthieu Cord
Transformers have been matching deep convolutional networks for vision architectures in recent works. Most work is focused on getting the best results on large-scale benchmarks, an…
Diverse Weight Averaging for Out-of-Distribution Generalization
Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier +3
Standard neural networks struggle to generalize under distribution shifts in computer vision. Fortunately, combining multiple networks can consistently improve out-of-distribution…
Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation
Antoine Saporta, Arthur Douillard, Tuan-Hung Vu +2
Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional…
Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval
Mustafa Shukor, Guillaume Couairon, Asya Grechka +1
Cross-modal image-recipe retrieval has gained significant attention in recent years. Most work focuses on improving cross-modal embeddings using unimodal encoders, that allow for e…
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…
SPIQ: Data-Free Per-Channel Static Input Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community.…
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…
FlexIT: Towards Flexible Semantic Image Translation
Guillaume Couairon, Asya Grechka, Jakob Verbeek +2
Deep generative models, like GANs, have considerably improved the state of the art in image synthesis, and are able to generate near photo-realistic images in structured domains su…
REx: Data-Free Residual Quantization Error Expansion
Edouard Yvinec, Arnaud Dapgony, Matthieu Cord +1
Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, w…