- Model architecture: Multimodal models often require more sophisticated architectures to handle the complexity of the data. This can include:
- Convolutional neural networks (CNNs): CNNs are commonly used for image and video processing and can be computationally expensive to train.
- Recurrent Neural networks (RNNs): RNNs are commonly used for sequential data such as speech and text, and can be computationally expensive to train.
- Attention mechanisms: Attention mechanisms are often used in multimodal models to focus on specific parts of the input data. This can add complexity and computational requirements to the model.
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