Multimodal AI refers to systems that process and integrate multiple types of data, such as text, images, audio, and video. Training and developing multimodal models require different techniques depending on the complexity and the type of modalities involved. This document outlines the different levels of multimodal AI development: Basic, Intermediate, and Advanced.
Definition: Combining multiple data types (e.g., text + images) to improve AI performance.
Applications: Image captioning, speech-to-text, visual question answering (VQA).
• Images: Resizing, normalization, and augmentation.
• Text: Tokenization, cleaning, and vectorization.
• Audio: Noise reduction, waveform transformation, and feature extraction (MFCCs, spectrograms).
Text: Word embeddings (Word2Vec, BERT, GPT).
Image: CNNs (ResNet, EfficientNet) for feature extraction.
Audio: Spectrogram analysis using RNNs or transformers.
• Early Fusion: Combines data at the input level (e.g., concatenating image and text embeddings).
• Late Fusion: Combines outputs of separate unimodal models (e.g., averaging prediction scores).
• Convolutional Neural Networks (CNNs) + LSTMs for image captioning.
• Transformer-based approaches (e.g., BERT + ResNet for VQA).
• Simple concatenation of embeddings for early fusion.
This stage involves more complex architectures and better modality integration.
Vision-Language Models: CLIP (Contrastive Language-Image Pretraining) for image-text alignment. o BLIP (Bootstrapped Language-Image Pretraining) for zero-shot learning. o LLaVA (Large Language and Vision Assistant) for interactive multimodal tasks.
Self-attention across different modalities (e.g., aligning text and images in transformers).
Cross-attention layers for joint feature representation (used in models like BLIP, Flamingo).
Learning joint embeddings across multiple modalities.
Examples: Multimodal Contrastive Learning: Training embeddings by pulling together related pairs (e.g., CLIP). Joint Feature Learning: Creating shared latent space for different modalities.
Hybrid Fusion: Combining early and late fusion for better performance.
Modality-Specific Branching: Allowing individual modalities to contribute differently depending on task importance.
Gated Multimodal Units: Controlling information flow between modalities.
• Pretraining on large multimodal datasets (e.g., LAION-5B for image-text models).
• Fine-tuning on downstream tasks (e.g., image captioning, medical AI, robotics perception).
• Transfer learning across modalities (e.g., using pretrained vision encoders with text models).
This stage focuses on real-world deployment, large-scale models, and cutting-edge techniques.
• Flamingo (DeepMind): Combines vision and text with cross-attention mechanisms.
• GIT (Generalist Image-to-Text Transformer): Unified model for various vision-language tasks.
• GPT-4V (OpenAI): Expands GPT-4 capabilities to process and generate images alongside text.
Using structured prompts with images, text, and speech for improved understanding.
Few-shot learning with multimodal contexts.
Episodic memory in AI assistants to improve long-term interaction.
Retrieval-Augmented Generation (RAG) using multimodal data sources.
Adversarial attacks: Testing models against input perturbations (e.g., altered images, noisy audio).
Robustness techniques: Training with diverse data, contrastive learning, multimodal augmentation.
Autonomous Agents: AI systems using multimodal inputs for real-world decisions (e.g., Tesla’s self-driving AI).
Healthcare AI: Combining radiology images with clinical notes for diagnosis.
Human-AI Interaction: Chatbots with visual, textual, and speech capabilities (e.g., multimodal assistants).
| Level | Key Techniques | Examples/Models |
|---|---|---|
| Basic | Feature Extraction, Early & Late Fusion | CNN + LSTMs, Simple Transformer Fusion |
| Basic | Tokenization, Spectrogram Analysis | Word2Vec, MFCCs, ResNet |
| Intermediate | Vision-Language Pretraining, Cross-Modality Attention | CLIP, BLIP, LLaVA |
| Intermediate | Multimodal Contrastive Learning, Joint Feature Learning | CLIP, Multimodal Transformers |
| Intermediate | Hybrid Fusion, Gated Multimodal Units | BLIP, Flamingo |
| Advanced | Large-Scale Pretrained Models | GPT-4V, Flamingo, GIT |
| Advanced | Adversarial Robustness, Multimodal RAG | Self-Supervised Learning |
| Advanced | Autonomous Agents, AI for Healthcare | Tesla AI, Radiology NLP Models |
