2k citations · 5.5k across the 40 of their papers we have counts for
8 papers · 1 filter
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…
Unsupervised Dense Information Retrieval with Contrastive Learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini +4
Recently, information retrieval has seen the emergence of dense retrievers, using neural networks, as an alternative to classical sparse methods based on term-frequency. These mode…
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…
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Mahmoud Assran, Mathilde Caron, Ishan Misra +4
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures t…
Self-supervised Pretraining of Visual Features in the Wild
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux +8
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment,…
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
Lina Mezghani, Sainbayar Sukhbaatar, Thibaut Lavril +4
In this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization…