353 citations · 462 across the 10 of their papers we have counts for
7 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…
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…
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero +7
Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose,…
Hierarchical Video Generation for Complex Data
Lluis Castrejon, Nicolas Ballas, Aaron Courville
Videos can often be created by first outlining a global description of the scene and then adding local details. Inspired by this we propose a hierarchical model for video generatio…
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Mahmoud Assran, Mathilde Caron, Ishan Misra +4
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures t…
Needles in Haystacks: On Classifying Tiny Objects in Large Images
Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas +3
In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional N…