# Design exercise: Multimodal Models

> Joint text, image, audio, and video representations, encoders, projectors, fusion, generation, and cross-modal evaluation. Implement designOutline() returning non-empty values for: modalityEncoders, fusion, crossModalEval. Each value must name a concrete mechanism or decision.

Canonical: https://learn.significanthobbies.com/curriculum/drills/practice-multimodal-models

Joint text, image, audio, and video representations, encoders, projectors, fusion, generation, and cross-modal evaluation. Implement designOutline() returning non-empty values for: modalityEncoders, fusion, crossModalEval. Each value must name a concrete mechanism or decision.

## The mechanism

Multimodal models align different signal spaces through shared or connected representations. Data alignment, modality-specific encoders, fusion, and cross-modal evals are central.

## Expected evidence

A design outline with modalityEncoders, fusion, crossModalEval plus an explicit failure mode or trade-off.

## Hints

- Name the failure mode before selecting the mechanism.

## Sources and practice

- [CLIP: Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)
- [Study Multimodal Models](https://learn.significanthobbies.com/curriculum/concepts/multimodal-models)
- [Try the interactive drill](https://learn.significanthobbies.com/drills/practice-multimodal-models)
- [All practice briefs](https://learn.significanthobbies.com/curriculum/drills/)