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no code implementations • 28 Sep 2023 • Timothée Darcet, Maxime Oquab, Julien Mairal, Piotr Bojanowski

Transformers have recently emerged as a powerful tool for learning visual representations.

1 code implementation • 18 Aug 2023 • Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot

Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.

1 code implementation • 9 Aug 2023 • Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot

Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex.

1 code implementation • 26 Jun 2023 • Gaspard Beugnot, Julien Mairal, Alessandro Rudi

We present a novel approach to non-convex optimization with certificates, which handles smooth functions on the hypercube or on the torus.

no code implementations • 21 Jun 2023 • Olivier Flasseur, Théo Bodrito, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange

Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star.

no code implementations • CVPR 2023 • Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal

Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually.

1 code implementation • CVPR 2023 • Enrico Fini, Pietro Astolfi, Karteek Alahari, Xavier Alameda-Pineda, Julien Mairal, Moin Nabi, Elisa Ricci

Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations.

1 code implementation • 21 Apr 2023 • Romain Menegaux, Emmanuel Jehanno, Margot Selosse, Julien Mairal

We introduce a novel self-attention mechanism, which we call CSA (Chromatic Self-Attention), which extends the notion of attention scores to attention _filters_, independently modulating the feature channels.

5 code implementations • 14 Apr 2023 • Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.

Ranked #1 on Image Retrieval on AmsterTime (using extra training data)

1 code implementation • 23 Feb 2023 • Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard

Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.

no code implementations • 16 Nov 2022 • Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid

Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.

1 code implementation • 22 Sep 2022 • Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti, Jocelyn Chanussot, Julien Mairal

In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.

no code implementations • 29 Jul 2022 • Bruno Lecouat, Thomas Eboli, Jean Ponce, Julien Mairal

Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas.

no code implementations • 24 Jun 2022 • Nassim Ait Ali Braham, Lichao Mou, Jocelyn Chanussot, Julien Mairal, Xiao Xiang Zhu

Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification.

no code implementations • 28 Feb 2022 • Gaspard Beugnot, Julien Mairal, Alessandro Rudi

This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms.

no code implementations • ICLR 2022 • Moulik Choraria, Leello Tadesse Dadi, Grigorios Chrysos, Julien Mairal, Volkan Cevher

Inspired by such studies, we conduct a spectral analysis of the Neural Tangent Kernel (NTK) of PNNs.

1 code implementation • 11 Feb 2022 • Houssam Zenati, Alberto Bietti, Eustache Diemert, Julien Mairal, Matthieu Martin, Pierre Gaillard

While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.

1 code implementation • CVPR 2022 • Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal

Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale.

no code implementations • ICLR 2022 • Michael Arbel, Julien Mairal

We study a class of algorithms for solving bilevel optimization problems in both stochastic and deterministic settings when the inner-level objective is strongly convex.

2 code implementations • NeurIPS 2021 • Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal

Hyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics.

no code implementations • NeurIPS 2021 • Gaspard Beugnot, Julien Mairal, Alessandro Rudi

The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels.

no code implementations • 15 Jun 2021 • Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid

Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal.

1 code implementation • 10 Jun 2021 • Grégoire Mialon, Dexiong Chen, Margot Selosse, Julien Mairal

We show that viewing graphs as sets of node features and incorporating structural and positional information into a transformer architecture is able to outperform representations learned with classical graph neural networks (GNNs).

no code implementations • 7 Jun 2021 • Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou

This paper reviews the NTIRE2021 challenge on burst super-resolution.

no code implementations • NeurIPS 2021 • Gaspard Beugnot, Julien Mairal, Alessandro Rudi

The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels.

23 code implementations • ICCV 2021 • Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).

Ranked #2 on Visual Place Recognition on Laurel Caverns

no code implementations • ICCV 2021 • Bruno Lecouat, Jean Ponce, Julien Mairal

This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time.

1 code implementation • NeurIPS 2020 • Bruno Lecouat, Jean Ponce, Julien Mairal

We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm.

1 code implementation • ICLR 2021 • Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont, Julien Mairal

We address the problem of learning on sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range dependencies, and possibly few labeled data.

15 code implementations • NeurIPS 2020 • Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin

In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much.

Ranked #1 on Contrastive Learning on imagenet-1k

1 code implementation • 22 Apr 2020 • Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal

Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.

1 code implementation • ECCV 2020 • Nikita Dvornik, Cordelia Schmid, Julien Mairal

Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples.

Ranked #4 on Few-Shot Image Classification on Meta-Dataset Rank

1 code implementation • ICML 2020 • Dexiong Chen, Laurent Jacob, Julien Mairal

On the other hand, our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks.

no code implementations • 10 Jan 2020 • Mathilde Caron, Ari Morcos, Piotr Bojanowski, Julien Mairal, Armand Joulin

In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i. e. on self-supervised tasks).

1 code implementation • 17 Dec 2019 • Julien Mairal

Cyanure is an open-source C++ software package with a Python interface.

1 code implementation • 5 Dec 2019 • Grégoire Mialon, Alexandre d'Aspremont, Julien Mairal

We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees.

1 code implementation • ECCV 2020 • Bruno Lecouat, Jean Ponce, Julien Mairal

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling.

no code implementations • 25 Sep 2019 • Mathilde Caron, Ari Morcos, Piotr Bojanowski, Julien Mairal, Armand Joulin

The lottery ticket hypothesis argues that neural networks contain sparse subnetworks, which, if appropriately initialized (the winning tickets), are capable of matching the accuracy of the full network when trained in isolation.

1 code implementation • NeurIPS 2019 • Dexiong Chen, Laurent Jacob, Julien Mairal

Substring kernels are classical tools for representing biological sequences or text.

1 code implementation • NeurIPS 2019 • Andrei Kulunchakov, Julien Mairal

In this paper, we introduce various mechanisms to obtain accelerated first-order stochastic optimization algorithms when the objective function is convex or strongly convex.

1 code implementation • NeurIPS 2019 • Alberto Bietti, Julien Mairal

State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties.

no code implementations • 7 May 2019 • Andrei Kulunchakov, Julien Mairal

In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Nesterov.

2 code implementations • ICCV 2019 • Mathilde Caron, Piotr Bojanowski, Julien Mairal, Armand Joulin

Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available.

Ranked #63 on Self-Supervised Image Classification on ImageNet (finetuned) (using extra training data)

1 code implementation • ICCV 2019 • Nikita Dvornik, Cordelia Schmid, Julien Mairal

Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples.

no code implementations • 25 Jan 2019 • Andrei Kulunchakov, Julien Mairal

In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Nesterov.

1 code implementation • 30 Sep 2018 • Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal

We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS).

no code implementations • 27 Sep 2018 • Alberto Bietti*, Grégoire Mialon*, Julien Mairal

In this work, we study the connection between regularization and robustness of deep neural networks by viewing them as elements of a reproducing kernel Hilbert space (RKHS) of functions and by regularizing them using the RKHS norm.

1 code implementation • 17 Sep 2018 • Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux

Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework.

no code implementations • 6 Sep 2018 • Nikita Dvornik, Julien Mairal, Cordelia Schmid

In this work, we consider object detection, semantic and instance segmentation and augment the training images by blending objects in existing scenes, using instance segmentation annotations.

2 code implementations • ECCV 2018 • Nikita Dvornik, Julien Mairal, Cordelia Schmid

For this approach to be successful, we show that modeling appropriately the visual context surrounding objects is crucial to place them in the right environment.

no code implementations • NeurIPS 2018 • Daan Wynen, Cordelia Schmid, Julien Mairal

In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings.

1 code implementation • 15 Dec 2017 • Hongzhou Lin, Julien Mairal, Zaid Harchaoui

One of the keys to achieve acceleration in theory and in practice is to solve these sub-problems with appropriate accuracy by using the right stopping criterion and the right warm-start strategy.

no code implementations • NeurIPS 2017 • Alberto Bietti, Julien Mairal

In this paper, we study deep signal representations that are near-invariant to groups of transformations and stable to the action of diffeomorphisms without losing signal information.

no code implementations • NeurIPS 2017 • Alberto Bietti, Julien Mairal

Stochastic optimization algorithms with variance reduction have proven successful for minimizing large finite sums of functions.

1 code implementation • NeurIPS 2017 • Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux

Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets.

2 code implementations • ICCV 2017 • Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, Cordelia Schmid

Real-time scene understanding has become crucial in many applications such as autonomous driving.

Ranked #2 on Real-Time Object Detection on PASCAL VOC 2007

1 code implementation • 9 Jun 2017 • Alberto Bietti, Julien Mairal

The success of deep convolutional architectures is often attributed in part to their ability to learn multiscale and invariant representations of natural signals.

no code implementations • 31 Mar 2017 • Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaid Harchaoui

We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions.

1 code implementation • 19 Jan 2017 • Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux

We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns.

1 code implementation • 30 Nov 2016 • Arthur Mensch, Julien Mairal, Gaël Varoquaux, Bertrand Thirion

We present a matrix factorization algorithm that scales to input matrices that are large in both dimensions (i. e., that contains morethan 1TB of data).

1 code implementation • 4 Oct 2016 • Hongzhou Lin, Julien Mairal, Zaid Harchaoui

We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms.

1 code implementation • NeurIPS 2017 • Alberto Bietti, Julien Mairal

Stochastic optimization algorithms with variance reduction have proven successful for minimizing large finite sums of functions.

no code implementations • NeurIPS 2016 • Julien Mairal

In this paper, we introduce a new image representation based on a multilayer kernel machine.

1 code implementation • 3 May 2016 • Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux

Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.

Ranked #12 on Recommendation Systems on MovieLens 10M

no code implementations • 1 Mar 2016 • Mattis Paulin, Julien Mairal, Matthijs Douze, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid

Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision.

no code implementations • 6 Feb 2016 • Andreas M. Tillmann, Yonina C. Eldar, Julien Mairal

We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase.

no code implementations • ICCV 2015 • Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronin, Cordelia Schmid

Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval.

no code implementations • 12 Nov 2014 • Julien Mairal, Francis Bach, Jean Ponce

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications.

no code implementations • NeurIPS 2014 • Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid

An important goal in visual recognition is to devise image representations that are invariant to particular transformations.

Ranked #23 on Image Classification on MNIST

no code implementations • CVPR 2014 • Anoop Cherian, Julien Mairal, Karteek Alahari, Cordelia Schmid

In this paper, we present a method for estimating articulated human poses in videos.

1 code implementation • CVPR 2014 • Yuansi Chen, Julien Mairal, Zaid Harchaoui

We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization.

no code implementations • 5 Mar 2014 • Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell

Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain.

Ranked #35 on Weakly Supervised Object Detection on PASCAL VOC 2007

no code implementations • 18 Feb 2014 • Julien Mairal

We present convergence guarantees for non-convex and convex optimization when the upper bounds approximate the objective up to a smooth error; we call such upper bounds "first-order surrogate functions".

no code implementations • NeurIPS 2013 • Julien Mairal

Majorization-minimization algorithms consist of iteratively minimizing a majorizing surrogate of an objective function.

no code implementations • 14 May 2013 • Julien Mairal

In this paper, we study optimization methods consisting of iteratively minimizing surrogates of an objective function.

no code implementations • 20 Apr 2012 • Julien Mairal, Bin Yu

We consider supervised learning problems where the features are embedded in a graph, such as gene expressions in a gene network.

no code implementations • NeurIPS 2010 • Julien Mairal, Rodolphe Jenatton, Francis R. Bach, Guillaume R. Obozinski

Our algorithm scales up to millions of groups and variables, and opens up a whole new range of applications for structured sparse models.

no code implementations • 27 Sep 2010 • Julien Mairal, Francis Bach, Jean Ponce

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing.

no code implementations • NeurIPS 2008 • Julien Mairal, Jean Ponce, Guillermo Sapiro, Andrew Zisserman, Francis R. Bach

It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data.

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