Multimodal artificial intelligence faces fundamental challenges in effectively integrating and processing diverse data types simultaneously. Current methodologies predominantly rely on late-fusion strategies, where separately pre-trained unimodal models are grafted together, such as attaching vision encoders to language models. This approach, while convenient, raises critical questions about optimality for true multimodal understanding. The inherent biases from […]
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