1.1k citations · 1.2k across the 13 of their papers we have counts for
15 papers
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec +6
Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains highly debated. In particular,…
Efficient Universal Perception Encoder
Chenchen Zhu, Saksham Suri, Cijo Jose +8
Running AI models on smart edge devices can unlock versatile user experiences, but presents challenges due to limited compute and the need to handle multiple tasks simultaneously.…
Disentangling the Factors of Convergence between Brains and Computer Vision Models
Joséphine Raugel, Marc Szafraniec, Huy V. Vo +5
Many AI models trained on natural images develop representations that resemble those of the human brain. However, the factors that drive this brain-model similarity remain poorly u…
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…
Back to the Features: DINO as a Foundation for Video World Models
Federico Baldassarre, Marc Szafraniec, Basile Terver +6
We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and trainin…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan +27
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-s…
DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment
Cijo Jose, Théo Moutakanni, Dahyun Kang +11
Self-supervised visual foundation models produce powerful embeddings that achieve remarkable performance on a wide range of downstream tasks. However, unlike vision-language models…
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Huy V. Vo, Vasil Khalidov, Timothée Darcet +10
Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically requir…
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
Théo Moutakanni, Maxime Oquab, Marc Szafraniec +2
Self-Supervised learning (SSL) with Joint-Embedding Architectures (JEA) has led to outstanding performances. All instantiations of this paradigm were trained using strong and well-…
Better (pseudo-)labels for semi-supervised instance segmentation
François Porcher, Camille Couprie, Marc Szafraniec +1
Despite the availability of large datasets for tasks like image classification and image-text alignment, labeled data for more complex recognition tasks, such as detection and segm…
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…
Code Translation with Compiler Representations
Marc Szafraniec, Baptiste Roziere, Hugh Leather +3
In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted r…
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Baptiste Roziere, Marie-Anne Lachaux, Marc Szafraniec +1
Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks. However, research in language model pre-training has mostly…
Continuous Surface Embeddings
Natalia Neverova, David Novotny, Vasil Khalidov +3
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories. While this problem has been considered before, solutions so f…
Putting Self-Supervised Token Embedding on the Tables
Marc Szafraniec, Gautier Marti, Philippe Donnat
Information distribution by electronic messages is a privileged means of transmission for many businesses and individuals, often under the form of plain-text tables. As their numbe…