Module 5 Overview: Multimodal Vision-Language Models

Time: 12-18 hours over 2 weeks

Learning Objectives

After completing this module, you will be able to:

• Multimodal Fusion Understanding: Master different strategies for combining vision and language: early fusion, late fusion, and cross-modal attention
• CLIP Architecture Mastery: Deeply understand contrastive language-image pretraining and how it creates aligned embedding spaces
• Vision Transformers (ViT): Learn how transformers apply to images through patch-based representations
• Direct Healthcare Application: Apply VLM techniques to combining clinical text with medical images or patient-reported symptoms

Why This Module Matters

This module is the direct precursor to multimodal healthcare AI research. You will learn how to combine vision and language representations into shared embedding spaces, understand contrastive learning with CLIP, and explore state-of-the-art VLM architectures.

Why multimodal learning is essential:

• Real-world data is inherently multimodal (images + text + structured data)
• CLIP enables zero-shot transfer without retraining
• Vision-language models power modern AI applications (GPT-4V, Gemini, Claude)
• Direct application to healthcare: radiology reports, symptom sketches, EHR + imaging

Connection to Healthcare AI

Multimodal learning is fundamental for healthcare:

• Medical Imaging + Reports: Radiology reports paired with X-rays/CT scans
• EHR + Imaging: Patient history combined with visual diagnosis
• Symptoms + Sketches: Patient-reported text with body area drawings
• Zero-Shot Diagnosis: CLIP-style models for rare conditions without labeled examples

This module teaches exactly the techniques needed for multimodal healthcare AI thesis work.

Prerequisites

Before starting this module:

• Module 2: Strong CNN understanding (vision encoders)
• Module 3: Deep transformer knowledge (text encoders)
• Module 4: Language model understanding (helpful but not required)
• Linear Algebra: Vector spaces, cosine similarity, projections

Module Path

Follow Multimodal Vision-Language Models Learning Path for the complete curriculum.

Key concepts covered:

1. Multimodal Foundations - Fusion strategies
2. Contrastive Learning - InfoNCE loss and self-supervision
3. Vision Transformer - Patch-based image encoding
4. CLIP Architecture - Contrastive language-image pre-training
5. Advanced VLMs - Flamingo, BLIP-2, LLaVA
6. Clinical VLMs - Medical imaging applications
7. VLM Applications - Real-world deployment

Critical Checkpoints

Must complete before proceeding to healthcare applications:

• ✅ Understand early vs late vs cross-modal fusion
• ✅ Can implement contrastive loss (InfoNCE) without references
• ✅ Understand how CLIP creates aligned embedding spaces
• ✅ Know why CLIP enables zero-shot transfer
• ✅ Understand Vision Transformer (ViT) patch encoding
• ✅ Can explain temperature parameter in contrastive learning
• ✅ Understand batch size importance for contrastive learning
• ✅ Know trade-offs between different VLM architectures

Time Breakdown

Total: 12-18 hours over 2 weeks

• Videos: 3-4 hours (CLIP paper explained, ViT tutorials)
• Reading: 4-6 hours (CLIP, ViT, BLIP-2, LLaVA papers)
• Implementation: 4-6 hours (Contrastive learning, CLIP from scratch)
• Exercises: 2-3 hours

Key Insights

Why CLIP Works:

• Natural Supervision: Web-scraped image-text pairs (400M examples)
• Contrastive Learning: Align image and text representations
• Zero-Shot Transfer: No retraining needed for new tasks
• Prompt Engineering: Task specification through text prompts

Why Contrastive Learning:

• Self-supervised from natural pairing (no manual labels)
• Scalable to millions/billions of examples
• Learns semantic similarity, not just classification
• Implicit hard negative mining within batch

Fusion Architecture Evolution:

1. Early Fusion: Concatenate features early (simple, limited interaction)
2. Late Fusion: Separate predictions, combine at end (robust to missing modalities)
3. Cross-Attention: Modalities attend to each other (rich interactions, state-of-the-art)

Key Takeaway

Multimodal learning is the future of AI.

Single-modality models are inherently limited. Human understanding combines vision, language, and other senses. Healthcare AI requires combining imaging, text notes, patient history, and lab results. Mastering multimodal fusion is essential for building AI that matches human-level understanding.

Next Steps

After completing this module:

1. Advanced: Generative Diffusion Models
2. Healthcare: Multimodal Healthcare Fusion
3. Healthcare: Clinical Vision-Language Models
4. Healthcare: Healthcare EHR Analysis Path

Ready to start? Begin with Multimodal Vision-Language Models Learning Path.