489 citations · 1.6k across the 31 of their papers we have counts for
32 papers
Guided Distillation for Semi-Supervised Instance Segmentation
Tariq Berrada, Camille Couprie, Karteek Alahari +1
Although instance segmentation methods have improved considerably, the dominant paradigm is to rely on fully-annotated training images, which are tedious to obtain. To alleviate th…
Multi-Domain Learning with Modulation Adapters
Ekaterina Iakovleva, Karteek Alahari, Jakob Verbeek
Deep convolutional networks are ubiquitous in computer vision, due to their excellent performance across different tasks for various domains. Models are, however, often trained in…
Zero-shot spatial layout conditioning for text-to-image diffusion models
Guillaume Couairon, Marlène Careil, Matthieu Cord +2
Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to…
Improved baselines for vision-language pre-training
Enrico Fini, Pietro Astolfi, Adriana Romero-Soriano +2
Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved impressive results by training on paired…
Controllable Image Generation via Collage Representations
Arantxa Casanova, Marlène Careil, Adriana Romero-Soriano +3
Recent advances in conditional generative image models have enabled impressive results. On the one hand, text-based conditional models have achieved remarkable generation quality,…
Are Visual Recognition Models Robust to Image Compression?
João Maria Janeiro, Stanislav Frolov, Alaaeldin El-Nouby +1
Reducing the data footprint of visual content via image compression is essential to reduce storage requirements, but also to reduce the bandwidth and latency requirements for trans…
Few-shot Semantic Image Synthesis with Class Affinity Transfer
Marlène Careil, Jakob Verbeek, Stéphane Lathuilière
Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of ima…
Instance-Conditioned GAN Data Augmentation for Representation Learning
Pietro Astolfi, Arantxa Casanova, Jakob Verbeek +3
Data augmentation has become a crucial component to train state-of-the-art visual representation models. However, handcrafting combinations of transformations that lead to improved…
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich +2
Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original. Theoretical results indicate that optimizing distortion metr…
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…
Unifying conditional and unconditional semantic image synthesis with OCO-GAN
Marlène Careil, Stéphane Lathuilière, Camille Couprie +1
Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more…
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…
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…
Instance-Conditioned GAN
Arantxa Casanova, Marlène Careil, Jakob Verbeek +2
Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as Image…
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…
Dual Mesh Convolutional Networks for Human Shape Correspondence
Nitika Verma, Adnane Boukhayma, Jakob Verbeek +1
Convolutional networks have been extremely successful for regular data structures such as 2D images and 3D voxel grids. The transposition to meshes is, however, not straight-forwar…
Learning Disentangled Representations with Reference-Based Variational Autoencoders
Adria Ruiz, Oriol Martinez, Xavier Binefa +1
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. So…
Adaptive Density Estimation for Generative Models
Thomas Lucas, Konstantin Shmelkov, Karteek Alahari +2
Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, an…
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Xiaotian Li, Juha Ylioinas, Jakob Verbeek +1
Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a quer…
Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
Maha Elbayad, Laurent Besacier, Jakob Verbeek
Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on…
Mixed batches and symmetric discriminators for GAN training
Thomas Lucas, Corentin Tallec, Jakob Verbeek +1
Generative adversarial networks (GANs) are pow- erful generative models based on providing feed- back to a generative network via a discriminator network. However, the discriminato…
Token-level and sequence-level loss smoothing for RNN language models
Maha Elbayad, Laurent Besacier, Jakob Verbeek
Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match t…