7 citations · 23 across the 19 of their papers we have counts for
8 papers · 1 filter
Multimodal Autoregressive Pre-training of Large Vision Encoders
Enrico Fini, Mustafa Shukor, Xiujun Li +13
We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framewo…
Skipping Computations in Multimodal LLMs
Mustafa Shukor, Matthieu Cord
Large Language Models (LLMs) have demonstrated remarkable success in both textual and multimodal domains. However, this success often comes with substantial computational costs, pa…
A Concept-Based Explainability Framework for Large Multimodal Models
Jayneel Parekh, Pegah Khayatan, Mustafa Shukor +2
Large multimodal models (LMMs) combine unimodal encoders and large language models (LLMs) to perform multimodal tasks. Despite recent advancements towards the interpretability of t…
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard +2
Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised image segmen…
Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs
Mustafa Shukor, Matthieu Cord
Large Language Models (LLMs) have demonstrated impressive performance on multimodal tasks, without any multimodal finetuning. They are the building block for Large Multimodal Model…
What Makes Multimodal In-Context Learning Work?
Folco Bertini Baldassini, Mustafa Shukor, Matthieu Cord +2
Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL…
Improved Baselines for Data-efficient Perceptual Augmentation of LLMs
Théophane Vallaeys, Mustafa Shukor, Matthieu Cord +1
The abilities of large language models (LLMs) have recently progressed to unprecedented levels, paving the way to novel applications in a wide variety of areas. In computer vision,…
FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models
Barbara Toniella Corradini, Mustafa Shukor, Paul Couairon +3
Foundation models have exhibited unprecedented capabilities in tackling many domains and tasks. Models such as CLIP are currently widely used to bridge cross-modal representations,…