Multimodal AI

Multimodal AI systems understand and generate content across multiple data types — text, images, audio, and video. CLIP showed that images and text can share a unified embedding space. GPT-4V, Gemini, and LLaVA demonstrated that language models can reason about visual content with near-human capability. This is now the frontier of AI products.

What is Multimodal Learning?

Traditional AI models are unimodal — a language model only processes text, a CNN only processes images. Multimodal models learn shared representations across modalities, enabling cross-modal retrieval, generation, and reasoning.

CLIP: Contrastive Language-Image Pre-training

OpenAI's CLIP (2021) is the foundational multimodal model. It was trained on 400 million image-text pairs scraped from the internet using a simple but powerful objective:

import torch
from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

inputs = processor(
    text=["a dog", "a cat", "a car"],
    images=image,
    return_tensors="pt",
    padding=True
)
outputs = model(**inputs)
probs = outputs.logits_per_image.softmax(dim=1)
# → tensor([[0.92, 0.07, 0.01]])  # it's a dog

Vision-Language Models (VLMs)

VLMs extend language models with the ability to process image inputs. The architecture connects a visual encoder to an LLM via a projection layer:

Key Models Compared

Multimodal Applications

Frequently Asked Questions