multimodal-fusion-models
Multimodal fusion models
📂 model-architectures
MODEL ARCHITECTURES#
Multimodal fusion models
Connect modality-specific encoders and generators through projection, cross-attention, shared token spaces, or combinations of these mechanisms.
MENTAL MODEL#
“Multimodal” names the inputs and outputs, not one topology. Always ask where modalities are encoded, fused, and decoded.
DATA FLOW#
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Text / image / audio / video
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Modality encoders or tokenizers
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Projector, cross-attention, or shared backbone
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Joint representation / language decoder
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Text, media, or action output
How it trains#
Systems combine contrastive alignment, captioning or next-token likelihood, masked objectives, paired instruction data, and sometimes separately pretrained frozen components.
How inference runs#
Inputs are encoded into modality tokens or features, fused before or inside the language/generative backbone, and decoded into one or more modalities. Input understanding does not imply output generation.
Strengths#
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• Grounds language in visual or acoustic inputs
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• Supports document, chart, image, video, and audio workflows
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• Can reuse strong pretrained modality components
Trade-offs#
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• Modality imbalance and connector bottlenecks
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• Media tokens consume substantial context and compute
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• Evaluation must separate perception, grounding, reasoning, and generation
Use it when#
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The task genuinely depends on non-text evidence
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Modality-specific slices and abstention are evaluated
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The architecture exposes enough detail for cost and privacy review
Avoid or challenge it when#
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A transcript, OCR result, or structured extractor is sufficient
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“Supports images” is assumed to mean precise perception
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Sensitive media handling and retention are undefined
Illustrative published families#
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• Flamingo cross-attention architecture
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• BLIP-2 learned querying connector
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• Shared-token multimodal decoder systems