Foundations of Multimodal AI: Fusions, Architectures, and Beyond

Abstract

A deep technical tour of multimodal AI: what 'multimodal' really means, the fusion techniques (early, middle, late) that combine modalities, the architectural evolution from RNNs and CNNs to Transformers and modern vision-language models (CLIP, LLaVA, BLIP-2, Qwen2-VL), plus datasets, training, evaluation, and where the field is heading.

Outline

1. What is multimodality (human-centered vs machine-centric)
2. Fusion techniques: early, middle, and late fusion
3. Architecture evolution: RNN, CNN, Transformer, ViT
4. Large vision-language models: CLIP, LLaVA, BLIP-2, MiniGPT-4, InstructBLIP
5. Datasets for alignment and instruction tuning
6. Training, evaluation metrics, and MLLM benchmarks
7. The future: reasoning, multilingual, mobile-first, RAG

Key takeaways

• Fusion strategy (early/middle/late) is the core design decision
• A projection layer is what bridges a vision encoder to an LLM
• Benchmarks reveal a 'blind faith in text' failure mode in VLMs