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As the AI community increasingly adopts large-scale models, it is crucial todevelop general and flexible tools to integrate them. We introduceGather-Attend-Scatter (GATS), a novel module that enables seamless combinationof pretrained foundation models, both trainable and frozen, into largermultimodal networks. GATS empowers AI systems to process and generateinformation across multiple modalities at different rates. In contrast totraditional fine-tuning, GATS allows for the original component models toremain frozen, avoiding the risk of them losing important knowledge acquiredduring the pretraining phase. We demonstrate the utility and versatility ofGATS with a few experiments across games, robotics, and multimodal input-outputsystems.

GATS: Gather-Attend-Scatter