2k citations · 6.6k across the 78 of their papers we have counts for
86 papers
DINOv3
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer +23
Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. B…
Cluster and Predict Latent Patches for Improved Masked Image Modeling
Timothée Darcet, Federico Baldassarre, Maxime Oquab +2
Masked Image Modeling (MIM) offers a promising approach to self-supervised representation learning, however existing MIM models still lag behind the state-of-the-art. In this paper…
Combining statistical learning with deep learning for improved exoplanet detection and characterization
Olivier Flasseur, Théo Bodrito, Julien Mairal +3
In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In s…
Functional Bilevel Optimization for Machine Learning
Ieva Petrulionyte, Julien Mairal, Michael Arbel
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. Thes…
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation
Bruno Lecouat, Yann Dubois de Mont-Marin, Théo Bodrito +2
This paper introduces a novel approach to the fine alignment of images in a burst captured by a handheld camera. In contrast to traditional techniques that estimate two-dimensional…
Towards Real-World Focus Stacking with Deep Learning
Alexandre Araujo, Jean Ponce, Julien Mairal
Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shall…
Vision Transformers Need Registers
Timothée Darcet, Maxime Oquab, Julien Mairal +1
Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised…
SUnAA: Sparse Unmixing using Archetypal Analysis
Behnood Rasti, Alexandre Zouaoui, Julien Mairal +1
This paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis. We assume that the endmembers of…
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Behnood Rasti, Alexandre Zouaoui, Julien Mairal +1
Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and inti…
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast
Olivier Flasseur, Théo Bodrito, Julien Mairal +3
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…
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
Juliette Marrie, Michael Arbel, Diane Larlus +1
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…
Semi-supervised learning made simple with self-supervised clustering
Enrico Fini, Pietro Astolfi, Karteek Alahari +4
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partiall…
GloptiNets: Scalable Non-Convex Optimization with Certificates
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. Unlike traditional methods that rely on a…
Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers
Romain Menegaux, Emmanuel Jehanno, Margot Selosse +1
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 modu…
DINOv2: Learning Robust Visual Features without Supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni +23
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. Thes…
deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast
Olivier Flasseur, Théo Bodrito, Julien Mairal +3
Direct imaging is an active research topic in astronomy for the detection and the characterization of young sub-stellar objects. The very high contrast between the host star and it…
Sequential Counterfactual Risk Minimization
Houssam Zenati, Eustache Diemert, Matthieu Martin +2
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. In this…
Learning Reward Functions for Robotic Manipulation by Observing Humans
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human vi…
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti +2
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural fo…
High Dynamic Range and Super-Resolution from Raw Image Bursts
Bruno Lecouat, Thomas Eboli, Jean Ponce +1
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 satu…
Non-Convex Bilevel Games with Critical Point Selection Maps
Michael Arbel, Julien Mairal
Bilevel optimization problems involve two nested objectives, where an upper-level objective depends on a solution to a lower-level problem. When the latter is non-convex, multiple…
Self Supervised Learning for Few Shot Hyperspectral Image Classification
Nassim Ait Ali Braham, Lichao Mou, Jocelyn Chanussot +2
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize…
The Spectral Bias of Polynomial Neural Networks
Moulik Choraria, Leello Tadesse Dadi, Grigorios Chrysos +2
Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Prev…
Efficient Kernel UCB for Contextual Bandits
Houssam Zenati, Alberto Bietti, Eustache Diemert +3
In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. While standard methods require a O(CT^3) complexity where T is the horizon…