2k citations · 6.6k across the 78 of their papers we have counts for
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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…