4 papers
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…
When LLaVA Meets Objects: Token Composition for Vision-Language-Models
Soumya Jahagirdar, Walid Bousselham, Anna Kukleva +1
Current autoregressive Vision Language Models (VLMs) usually rely on a large number of visual tokens to represent images, resulting in a need for more compute especially at inferen…
REVEAL: Relation-based Video Representation Learning for Video-Question-Answering
Sofian Chaybouti, Walid Bousselham, Moritz Wolter +1
Video-Question-Answering (VideoQA) comprises the capturing of complex visual relation changes over time, remaining a challenge even for advanced Video Language Models (VLM), i.a.,…
VideoGEM: Training-free Action Grounding in Videos
Felix Vogel, Walid Bousselham, Anna Kukleva +2
Vision-language foundation models have shown impressive capabilities across various zero-shot tasks, including training-free localization and grounding, primarily focusing on local…