5 citations · 5 across the 2 of their papers we have counts for
3 papers
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace
Léopold Maytié, Benjamin Devillers, Alexandre Arnold +1
Humans perceive the world through multiple senses, enabling them to create a comprehensive representation of their surroundings and to generalize information across domains. For in…
Semi-supervised Multimodal Representation Learning through a Global Workspace
Benjamin Devillers, Léopold Maytié, Rufin VanRullen
Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations, or to translate signal…
Does language help generalization in vision models?
Benjamin Devillers, Bhavin Choksi, Romain Bielawski +1
Vision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets. A recent model (CLIP) was found to generalize well in zero-shot…