Search Results for author: Matthieu Cord

Found 81 papers, 48 papers with code

Embedding Arithmetic for Text-driven Image Transformation

no code implementations6 Dec 2021 Guillaume Couairon, Matthieu Cord, Matthijs Douze, Holger Schwenk

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man.

Text Matching

CSG0: Continual Urban Scene Generation with Zero Forgetting

no code implementations6 Dec 2021 Himalaya Jain, Tuan-Hung Vu, Patrick Pérez, Matthieu Cord

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving.

Continual Learning Scene Generation +1

RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks

no code implementations NeurIPS 2021 Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, Kevin Bailly

Deep Neural Networks (DNNs) are ubiquitous in today's computer vision landscape, despite involving considerable computational costs.

DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion

no code implementations22 Nov 2021 Arthur Douillard, Alexandre Ramé, Guillaume Couairon, Matthieu Cord

Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion.

Continual Learning

STEEX: Steering Counterfactual Explanations with Semantics

1 code implementation17 Nov 2021 Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord

As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns.

Counterfactual Explanation

RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging

no code implementations30 Sep 2021 Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, Kevin Bailly

Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration.

Raising context awareness in motion forecasting

no code implementations16 Sep 2021 Hédi Ben-Younes, Éloi Zablocki, Mickaël Chen, Patrick Pérez, Matthieu Cord

Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history.

Motion Forecasting Trajectory Prediction

LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR

no code implementations8 Sep 2021 Florent Bartoccioni, Éloi Zablocki, Patrick Pérez, Matthieu Cord, Karteek Alahari

In such a monocular setup, dense depth is obtained with either additional input from one or several expensive LiDARs, e. g., with 64 beams, or camera-only methods, which suffer from scale-ambiguity and infinite-depth problems.

Depth Completion Depth Estimation

Fishr: Invariant Gradient Variances for Out-of-distribution Generalization

2 code implementations7 Sep 2021 Alexandre Rame, Corentin Dancette, Matthieu Cord

In this paper, we introduce a new regularization -- named Fishr -- that enforces domain invariance in the space of the gradients of the loss: specifically, the domain-level variances of gradients are matched across training domains.

Domain Generalization

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

1 code implementation ICCV 2021 Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez

In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time.

Semantic Segmentation Transfer Learning +1

Semantic Palette: Guiding Scene Generation with Class Proportions

1 code implementation CVPR 2021 Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain, Patrick Pérez, Matthieu Cord

Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem.

Data Augmentation Image Generation +1

RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks

no code implementations31 May 2021 Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, Kevin Bailly

Deep Neural Networks (DNNs) are ubiquitous in today's computer vision land-scape, despite involving considerable computational costs.

Going deeper with Image Transformers

10 code implementations ICCV 2021 Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, Hervé Jégou

In particular, we investigate the interplay of architecture and optimization of such dedicated transformers.

Ranked #3 on Image Classification on CIFAR-10 (using extra training data)

Image Classification Transfer Learning

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

2 code implementations CVPR 2021 Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, Patrick Pérez

With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image.

Object Detection Representation Learning +4

Confidence Estimation via Auxiliary Models

no code implementations11 Dec 2020 Charles Corbière, Nicolas Thome, Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez

In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).

Domain Adaptation Image Classification +1

Driving Behavior Explanation with Multi-level Fusion

1 code implementation9 Dec 2020 Hédi Ben-Younes, Éloi Zablocki, Patrick Pérez, Matthieu Cord

In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions.

Explainable artificial intelligence Trajectory Prediction

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

1 code implementation4 Dec 2020 Taylor Mordan, Matthieu Cord, Patrick Pérez, Alexandre Alahi

By increasing the number of attributes jointly learned, we highlight an issue related to the scales of gradients, which arises in MTL with numerous tasks.

Autonomous Driving Multi-Task Learning

Powers of layers for image-to-image translation

no code implementations13 Aug 2020 Hugo Touvron, Matthijs Douze, Matthieu Cord, Hervé Jégou

We propose a simple architecture to address unpaired image-to-image translation tasks: style or class transfer, denoising, deblurring, deblocking, etc.

 Ranked #1 on Image-to-Image Translation on horse2zebra (Frechet Inception Distance metric)

Deblurring Denoising +2

Insights from the Future for Continual Learning

1 code implementation24 Jun 2020 Arthur Douillard, Eduardo Valle, Charles Ollion, Thomas Robert, Matthieu Cord

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting.

class-incremental learning Representation Learning +1

Overcoming Statistical Shortcuts for Open-ended Visual Counting

1 code implementation17 Jun 2020 Corentin Dancette, Remi Cadene, Xinlei Chen, Matthieu Cord

First, we propose the Modifying Count Distribution (MCD) protocol, which penalizes models that over-rely on statistical shortcuts.

ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation

1 code implementation15 Jun 2020 Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez

While fully-supervised deep learning yields good models for urban scene semantic segmentation, these models struggle to generalize to new environments with different lighting or weather conditions for instance.

Self-Supervised Learning Semantic Segmentation +1

PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning

1 code implementation ECCV 2020 Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, Eduardo Valle

Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning.

class-incremental learning Incremental Learning +1

Handling new target classes in semantic segmentation with domain adaptation

1 code implementation2 Apr 2020 Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez

In this work, we define and address a novel domain adaptation (DA) problem in semantic scene segmentation, where the target domain not only exhibits a data distribution shift w. r. t.

Scene Segmentation Universal Domain Adaptation +2

Learning Representations by Predicting Bags of Visual Words

1 code implementation CVPR 2020 Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, Matthieu Cord

Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words.

Representation Learning

QUEST: Quantized embedding space for transferring knowledge

1 code implementation ECCV 2020 Himalaya Jain, Spyros Gidaris, Nikos Komodakis, Patrick Pérez, Matthieu Cord

Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network.

Knowledge Distillation

This dataset does not exist: training models from generated images

no code implementations7 Nov 2019 Victor Besnier, Himalaya Jain, Andrei Bursuc, Matthieu Cord, Patrick Pérez

This naturally brings the question: Can we train a classifier only on the generated data?

REVE: Regularizing Deep Learning with Variational Entropy Bound

no code implementations15 Oct 2019 Antoine Saporta, Yifu Chen, Michael Blot, Matthieu Cord

Studies on generalization performance of machine learning algorithms under the scope of information theory suggest that compressed representations can guarantee good generalization, inspiring many compression-based regularization methods.

Riemannian batch normalization for SPD neural networks

no code implementations NeurIPS 2019 Daniel Brooks, Olivier Schwander, Frederic Barbaresco, Jean-Yves Schneider, Matthieu Cord

Covariance matrices have attracted attention for machine learning applications due to their capacity to capture interesting structure in the data.

Action Recognition

Boosting Few-Shot Visual Learning with Self-Supervision

1 code implementation ICCV 2019 Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, Matthieu Cord

Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data.

Few-Shot Learning Self-Supervised Learning

DualDis: Dual-Branch Disentangling with Adversarial Learning

1 code implementation3 Jun 2019 Thomas Robert, Nicolas Thome, Matthieu Cord

To effectively separate the information, we propose to use a combination of regular and adversarial classifiers to guide the two branches in specializing for class and attribute information respectively.

Data Augmentation Image Manipulation +1

SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss

no code implementations6 May 2019 Yifu Chen, Arnaud Dapogny, Matthieu Cord

As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects.

Edge Detection Semantic Segmentation

DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wild

no code implementations ICCV 2019 Arnaud Dapogny, Kévin Bailly, Matthieu Cord

Face Alignment is an active computer vision domain, that consists in localizing a number of facial landmarks that vary across datasets.

Face Alignment

BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

1 code implementation31 Jan 2019 Hedi Ben-Younes, Rémi Cadene, Nicolas Thome, Matthieu Cord

We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities.

Question Answering Representation Learning +3

HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning

no code implementations ECCV 2018 Thomas Robert, Nicolas Thome, Matthieu Cord

In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet.

General Classification Image Classification

Images & Recipes: Retrieval in the cooking context

1 code implementation2 May 2018 Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Matthieu Cord

Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine.

Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

1 code implementation30 Apr 2018 Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord

Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.

Cross-Modal Retrieval

Finding beans in burgers: Deep semantic-visual embedding with localization

1 code implementation CVPR 2018 Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord

Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic relationships.

Cross-Modal Retrieval Image Captioning +1

GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange

no code implementations4 Apr 2018 Michael Blot, David Picard, Matthieu Cord

We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent.

Distributed Optimization

Deformable Part-based Fully Convolutional Network for Object Detection

no code implementations19 Jul 2017 Taylor Mordan, Nicolas Thome, Matthieu Cord, Gilles Henaff

Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular.

Object Detection

Gossip training for deep learning

1 code implementation29 Nov 2016 Michael Blot, David Picard, Matthieu Cord, Nicolas Thome

We address the issue of speeding up the training of convolutional networks.

Maxmin convolutional neural networks for image classification

no code implementations25 Oct 2016 Michael Blot, Matthieu Cord, Nicolas Thome

Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification.

General Classification Image Classification

Master's Thesis : Deep Learning for Visual Recognition

1 code implementation18 Oct 2016 Rémi Cadène, Nicolas Thome, Matthieu Cord

Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.

Closed-Form Training of Mahalanobis Distance for Supervised Clustering

no code implementations CVPR 2016 Marc T. Law, Yao-Liang Yu, Matthieu Cord, Eric P. Xing

Clustering is the task of grouping a set of objects so that objects in the same cluster are more similar to each other than to those in other clusters.

Metric Learning Structured Prediction

WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks

1 code implementation CVPR 2016 Thibaut Durand, Nicolas Thome, Matthieu Cord

In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON).

Multiple Instance Learning

Deep Neural Networks Under Stress

1 code implementation11 May 2016 Micael Carvalho, Matthieu Cord, Sandra Avila, Nicolas Thome, Eduardo Valle

In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets.

Transfer Learning

Fantope Regularization in Metric Learning

no code implementations CVPR 2014 Marc T. Law, Nicolas Thome, Matthieu Cord

This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning.

Face Verification General Classification +2

Top-Down Regularization of Deep Belief Networks

no code implementations NeurIPS 2013 Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim

We suggest a deep learning strategy that bridges the gap between the two phases, resulting in a three-phase learning procedure.

Object Recognition

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