118 citations · 135 across the 4 of their papers we have counts for
6 papers · 1 filter
Self-Masking Networks for Unsupervised Adaptation
Alfonso Taboada Warmerdam, Mathilde Caron, Yuki M. Asano
With the advent of billion-parameter foundation models, efficient fine-tuning has become increasingly important for the adaptation of models to downstream tasks. However, especiall…
A Generative Approach for Wikipedia-Scale Visual Entity Recognition
Mathilde Caron, Ahmet Iscen, Alireza Fathi +1
In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way…
Weakly-Supervised Surgical Phase Recognition
Roy Hirsch, Regev Cohen, Mathilde Caron +3
A key element of computer-assisted surgery systems is phase recognition of surgical videos. Existing phase recognition algorithms require frame-wise annotation of a large number of…
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Mostafa Dehghani, Basil Mustafa, Josip Djolonga +12
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challeng…
Verbs in Action: Improving verb understanding in video-language models
Liliane Momeni, Mathilde Caron, Arsha Nagrani +2
Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language mo…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…