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AI Core Concepts (Part 16): Multimodal Models
Multimodal models can process and generate multiple types of data — including text, images, audio, and video — either as inputs, outputs, or both. These models integrate information from different modalities for richer understanding and reasoning.
1. What Is a Multimodal Model?
A model that accepts or produces more than one type of input/output modality. Common modalities:
- 📝 Text
- 🖼️ Image
- 🔊 Audio
- 🎥 Video
- 🧠 Sensor data
2. Examples of Multimodal Use Cases
| Task | Modalities | Example |
|---|---|---|
| Visual Question Answering | Image + Text → Text | “What’s in this picture?” |
| Image Captioning | Image → Text | Describe what's in an image |
| Speech Recognition | Audio → Text | Transcribe audio recordings |
| Text-to-Image Generation | Text → Image | “Generate a cat riding a bike” |
| Video Summarization | Video → Text | Summarize a YouTube video |
| Audio-Visual Emotion Recognition | Audio + Video → Label | Detect mood in a video call |
3. Architecture: Combining Modalities
Multimodal models typically use:
- Separate encoders per modality (e.g., image encoder, text encoder)
- A fusion layer to combine modality embeddings
- A decoder to generate output in a target modality
# Pseudocode for architecture
text_embedding = TextEncoder(text_input)
image_embedding = ImageEncoder(image_input)
combined = FusionLayer([text_embedding, image_embedding])
output = Decoder(combined)
4. Famous Multimodal Models
| Model | Capabilities |
|---|---|
| CLIP | Connects images and text |
| DALL·E | Text-to-image generation |
| GPT-4o | Omni-modal (text, image, audio) |
| Flamingo | Visual-language QA |
| Gemini | Multimodal across web and tools |
| BLIP-2 | Image captioning + QA |
| SpeechT5 | Unified audio-text model |
5. Example: CLIP (Contrastive Language–Image Pretraining)
CLIP learns to match images with relevant text by training on image-caption pairs.
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import torch
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
image = Image.open("dog.jpg")
texts = ["a dog", "a cat", "a human"]
inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
print(probs) # Probability that the image matches each caption
6. Building a Simple Text + Image Model
# Encode text and image, then combine
text_emb = TextEncoder("a red apple")
image_emb = ImageEncoder("apple.jpg")
combined = torch.cat([text_emb, image_emb], dim=1)
output = Classifier(combined) # e.g., for matching or classification
7. Tools & Libraries for Multimodal AI
- 🤗 Hugging Face Transformers + Datasets
- 🧨 Diffusers – for generative models (DALL·E, Stable Diffusion)
- 🐍 PyTorch + torchvision
- 🧠 OpenAI’s CLIP and Whisper
- 🔥 Google DeepMind Flamingo / Gemini APIs (cloud-based)
8. Challenges of Multimodal Models
- Alignment: Matching info across modalities (e.g., image ↔ caption)
- Latency: Higher inference costs due to multiple encoders
- Data imbalance: Text data is more available than video/audio
- Complexity: Difficult to fine-tune or deploy on edge devices
9. Prompting Multimodal LLMs
For models like GPT-4o (text + image):
Prompt: "What's happening in this image?"
Attachment: 📷 [image of a flooded street]
For tools like LangChain:
from langchain.chains import MultiModalChain
# Combine a text prompt and image input for richer querying
📚 Further Resources
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