2k citations · 4.8k across the 35 of their papers we have counts for
7 papers · 1 filter
Uniform Masking Prevails in Vision-Language Pretraining
Siddharth Verma, Yuchen Lu, Rui Hou +4
Masked Language Modeling (MLM) has proven to be an essential component of Vision-Language (VL) pretraining. To implement MLM, the researcher must make two design choices: the maski…
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,…
Neural Attentive Circuits
Nasim Rahaman, Martin Weiss, Francesco Locatello +5
Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically m…
The Hidden Uniform Cluster Prior in Self-Supervised Learning
Mahmoud Assran, Randall Balestriero, Quentin Duval +6
A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g., SimCLR, VICReg, SwAV, MSN). We show tha…
Cascaded Video Generation for Videos In-the-Wild
Lluis Castrejon, Nicolas Ballas, Aaron Courville
Videos can be created by first outlining a global view of the scene and then adding local details. Inspired by this idea we propose a cascaded model for video generation which foll…
Masked Siamese Networks for Label-Efficient Learning
Mahmoud Assran, Mathilde Caron, Ishan Misra +6
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containi…
BARACK: Partially Supervised Group Robustness With Guarantees
Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas +4
While neural networks have shown remarkable success on classification tasks in terms of average-case performance, they often fail to perform well on certain groups of the data. Suc…