The right approach depends on your task (classification, retrieval, generation, forecasting, etc.), but there are a few architectures that consistently work well across multimodal projects.
Common fusion strategies
1. Early fusion
Combine all modalities into a shared representation before most of the model processes them.
Example:
- Text → transformer embedding
- Image → vision encoder embedding
- Tabular → MLP embedding
- Concatenate embeddings
- Feed into a shared transformer or MLP
Pros
- Learns cross-modal interactions early
- Simple architecture
Cons
- Sensitive to missing modalities
- Can become computationally expensive
Best when:
- All modalities are usually available
- Strong interactions exist between modalities
2. Late fusion
Train separate models for each modality and combine predictions.
Example:
Image model → prediction
Text model → prediction
Tabular model → prediction
Final prediction = weighted average / stacking / meta-model
Pros
- Robust
- Easy to debug
- Missing modalities are easier to handle
Cons
- Doesn't learn rich interactions between modalities
Best when:
- Different teams own different models
- Modalities vary in quality
3. Intermediate (hybrid) fusion
Encode each modality independently, then fuse latent representations.
Text Encoder
\
Cross-attention
/
Image Encoder
Tabular Encoder
|
Fusion block
|
Output
This is currently the most common approach in research.
Modern architectures
Cross-attention Transformers
Each modality gets its own encoder.
Then use cross-attention layers so:
- text attends to image
- image attends to text
- tabular attends to both
Examples include:
- LXMERT
- ViLBERT
- FLAVA
- BLIP-2
Good when relationships between modalities matter.
Shared embedding space
Project every modality into one latent space.
Image -> 512-d vector
Text -> 512-d vector
Table -> 512-d vector
Then perform downstream tasks using those embeddings.
Popular for:
- retrieval
- search
- recommendation
- clustering
Examples:
Mixture-of-Experts (MoE)
Assign specialized experts to different modalities.
Expert A: images
Expert B: text
Expert C: tables
Router decides which experts activate.
Useful for:
- large datasets
- efficient inference
- scaling to many modalities
Handling tabular data
Tabular data often benefits from a dedicated encoder rather than being treated as plain text.
Common options:
- MLP
- Gradient boosted trees
- TabTransformer
- FT-Transformer
- TabNet
Then fuse the resulting embedding with text and image embeddings.
If the table contains natural-language fields, you can also serialize rows into text for an LLM, though this may lose some numerical structure.
Popular open-source frameworks
Still the dominant research framework.
Useful libraries include:
- TorchMultimodal
- TorchVision
- PyTorch Lightning
Supports many multimodal models:
- BLIP
- IDEFICS
- LLaVA
- CLIP
- Florence
- Qwen-VL
Very strong ecosystem for experimentation.
Provides production-ready components for:
- vision
- detection
- segmentation
- multimodal learning
Designed for large-scale multimodal training and deployment.
Good if you're training on multiple GPUs.
Training tips
- Normalize each modality independently before fusion.
- Avoid one modality dominating by balancing feature scales or using modality-specific normalization.
- Handle missing modalities explicitly (e.g., masking, modality dropout).
- Use contrastive pretraining (similar to CLIP) if cross-modal retrieval is important.
- Augment each modality appropriately: image augmentations, text paraphrasing, and sensible perturbations for tabular data.
- Monitor per-modality performance in addition to overall metrics to identify bottlenecks.
Example architecture
Text
│
Text Transformer
│
Text embedding
│
│
Image ──► Vision Encoder ─► Image embedding
│
│
Table ──► Tabular Encoder ─► Table embedding
│
Cross-attention Fusion
│
Shared Transformer
│
Classification / Regression /
Retrieval / Generation
This "separate encoders + intermediate fusion" design is a strong default for many multimodal applications because it preserves modality-specific strengths while allowing the model to learn interactions where they matter.
If you can share your specific use case—for example, document understanding, medical diagnosis, recommendation systems, visual question answering, fraud detection, or another application—I can recommend architectures and models that are particularly well suited to that scenario.
