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You are building a multimodal model that combines text and images to generate product descriptions. The text data is tokenized using spaCy, and the image data is represented as feature vectors extracted from a pre-trained ResNet model. How can you effectively align and fuse these heterogeneous data types before feeding them into a downstream generative model?
Correct Answer: C,E
Direct concatenation or averaging doesn't capture the complex relationships between modalities. Cross-modal attention allows the model to learn which parts of the image are most relevant to the text, leading to better alignment and fusion. Projecting both modalities into a common embedding space allows for a unified representation that can be effectively used by the downstream generative model.