Hello! Lately, I've been getting interested in multimodal AI models that can handle multiple data types (text, images, audio, video). So, what approaches do you think yield more robust results when implementing these types of models? For example, how do you preprocess your data, how do you structure the model architecture, or which performance metrics do you prefer to focus on? Any general roadmap suggestions would be greatly appreciated!
What's the best approach for multimodal AI?
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How do you normalize your data when transitioning to multimodal AI? For example, what methods do you prefer to map the word "bank" in text-based data to the "bank" object in an image within the same embedding space? What are the practical challenges in preprocessing to address the different meanings of the same word in different contexts (e.g., "bank" as a financial institution vs. "bank" as a seating group)?
One of the fundamental issues we face in model architecture is how synchronized the preloading of different modalities is. Does using late fusion (processing modalities separately and then combining them) perform more efficiently than early fusion (directly linking features from each modality)? Which fusion strategy yields more reliable results, especially in text-image matching tasks?