Multimodal Foundation Models MOC
Multimodal foundation models learn across text, images, audio, video, and sometimes actions. Frontier labs are moving from chatbots to general systems that perceive and act.
Why It Matters
Text-only models cannot fully handle:
- Screenshots.
- Charts.
- Documents.
- Video.
- Speech.
- Robotics.
- UI interaction.
- Spatial reasoning.
Core Topics
Vision Transformers
Images can be split into patches and processed by transformer architectures.
Primary source:
Contrastive Image-Text Learning
CLIP-style training aligns images and text in a shared embedding space.
Primary source:
Vision-Language Models
Learn:
- Image encoders.
- Projectors/adapters.
- Cross-attention.
- Multimodal instruction tuning.
- OCR and document understanding.
- Visual question answering.
Diffusion And Flow Models
Generative image/video models often use diffusion or flow-matching-style objectives.
Primary sources:
Audio And Video
Learn:
- Spectrograms.
- Audio tokenization.
- Video frame sampling.
- Temporal attention.
- Streaming constraints.
Starter Project
Build a CLIP-style retrieval demo:
- Use an image-text embedding model.
- Embed a small image set.
- Query with text.
- Inspect failure cases.
- Add captions and compare retrieval quality.
Deliverable:
- A memo: "What does the embedding space understand and miss?"
Papers / Resources
What Good Looks Like
You can:
- Explain image patching.
- Explain contrastive loss.
- Distinguish embedding alignment from generative modeling.
- Understand why OCR/document tasks are hard.
- Evaluate hallucination in vision-language outputs.
- Reason about multimodal latency and context size.