AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The Fusion Anything Model (FAM) is proposed, a foundation model for generalized multimodal data fusion that encodes transferable multimodal correlations during training and activates appropriate associations through in-context examples during inference.
This paper uses a vision-language model (VLM) to compute similarities between target samples and text labels, including those for unknown classes generated by prompting a large language model, and proposes a framework that leverages foundation models (LFM) for SF-UniDA.
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