Multimodal Fusion
Combining information from multiple modalities (text, image, audio) to make a joint prediction or representation.
Think of it like
A deaf person reads captions while watching a video; a hearing person listens to audio. Both together understand better.
Example
Text "dog" + image of a dog -> stronger confidence that its a dog. Video frame + audio -> identify the action better.
How it actually works
Fusion can happen early (raw signals), mid (embeddings), or late (predictions). Early: mix modalities before processing. Mid: project each modality to shared space, combine. Late: predict separately, then ensemble. Alignment is crucial: align embeddings so related content (matching image-text pairs) is close. Challenges: missing modalities, domain gap, temporal/spatial alignment. Methods: dot-product attention, cross-attention, learned fusion networks, or contrastive objectives (like CLIP).
For product teams
Multimodal search (text + image query), accessible systems, robust recommendations.
For engineers
Fusion strategies: concatenation, element-wise, attention-based, learned gates. Alignment: contrastive loss or reconstruction loss.
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