14 citations · 41 across the 13 of their papers we have counts for
5 papers · 1 filter
DEX-AR: A Dynamic Explainability Method for Autoregressive Vision-Language Models
Walid Bousselham, Angie Boggust, Hendrik Strobelt +1
As Vision-Language Models (VLMs) become increasingly sophisticated and widely used, it becomes more and more crucial to understand their decision-making process. Traditional explai…
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
Walid Bousselham, Angie Boggust, Sofian Chaybouti +2
Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However,…
VisText: A Benchmark for Semantically Rich Chart Captioning
Benny J. Tang, Angie Boggust, Arvind Satyanarayan
Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities. Howeve…
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Brian Chen, Andrew Rouditchenko, Kevin Duarte +10
Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data…
AVLnet: Learning Audio-Visual Language Representations from Instructional Videos
Andrew Rouditchenko, Angie Boggust, David Harwath +11
Current methods for learning visually grounded language from videos often rely on text annotation, such as human generated captions or machine generated automatic speech recognitio…