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Found 2 papers, 1 papers with code
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace
First, we train a 'Global Workspace' to exploit information collected about the environment via two input modalities (a visual input, or an attribute vector representing the state of the agent and/or its environment).
Semi-supervised Multimodal Representation Learning through a Global Workspace
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 signals from one domain to another (as in image captioning, or text-to-image generation).
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