2k citations · 4.8k across the 35 of their papers we have counts for
5 papers · 1 filter
Stochastic positional embeddings improve masked image modeling
Amir Bar, Florian Bordes, Assaf Shocher +6
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. Despite its recent success, learning good representations…
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation
Florian Bordes, Samuel Lavoie, Randall Balestriero +2
Self-Supervised Learning (SSL) models rely on a pretext task to learn representations. Because this pretext task differs from the downstream tasks used to evaluate the performance…
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…
A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Quentin Duval, Ishan Misra, Nicolas Ballas
Self-supervised methods in vision have been mostly focused on large architectures as they seem to suffer from a significant performance drop for smaller architectures. In this pape…
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Mahmoud Assran, Quentin Duval, Ishan Misra +5
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedd…