37 citations · 98 across the 12 of their papers we have counts for
11 papers · 1 filter
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,…
Gradpaint: Gradient-Guided Inpainting with Diffusion Models
Asya Grechka, Guillaume Couairon, Matthieu Cord
Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. The pre-trained models can be adapted…
DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion
Cédric Rommel, Eduardo Valle, Mickaël Chen +4
We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively un…
PowerQuant: Automorphism Search for Non-Uniform Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of…
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
Mustafa Shukor, Guillaume Couairon, Matthieu Cord
Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks. The current trend is to move towards ever larger models and pretraining…