Models That Combine Text + Image + Audio • CLIP • Video Transformers
Multimodal Deep Learning is a modern approach where a model learns from more than one type of data at the same time.
A "modality" means a type of input — such as text, images, audio, or video.
Just like humans use eyes, ears, and language together to understand the world, multimodal AI combines multiple sources to understand information better.
1. What Is Multimodal Deep Learning?
Traditional models use only one type of data:
- Vision models → only images
- NLP models → only text
- Audio models → only sound
But real life is not single-modality.
Example
A YouTube video contains:
- Visual frames (images)
- Audio (speech, music)
- Text (subtitles or captions)
A multimodal model can understand all of them together.
Why It Is Powerful
- Better understanding
- More accurate predictions
- Works well in real-world applications
Real-World Examples
- Self-driving cars (camera + radar + lidar)
- Voice assistants (speech + text)
- Medical AI (MRI images + patient notes)
- Social media apps (image + caption understanding)
2. Models That Combine Text + Image + Audio
Multimodal models blend features from multiple inputs.
A. Text + Image Models
Use text and image together to:
- Describe pictures
- Generate images from text
- Match captions with correct images
Example: "A cat sitting on a chair."
B. Text + Audio Models
Used for:
- Speech-to-text
- Voice assistants
- Music classification
C. Image + Audio + Text (Three-Modality Models)
Used in:
- Video understanding
- Movie description generation
- Classroom lecture analysis
How They Work (Simple Explanation)
- Each modality is encoded separately
- Features are combined
- A multimodal layer learns connections
- The model predicts output
3. CLIP (Contrastive Language–Image Pretraining)
CLIP is one of the most important multimodal AI models created by OpenAI.
What CLIP Does
CLIP connects images and text.
It learns which text matches which image.
Example
You give CLIP:
- An image of a dog
- Two sentences:
- "A dog playing in the grass."
- "A car parked on the road."
CLIP chooses the correct description: Sentence 1.
Why CLIP Is Powerful
- Learns from 400 million image–text pairs
- Understands high-level concepts
- Works without labeled datasets
- Used in image search and text-to-image models (like DALL·E)
Simple CLIP Code Example (HuggingFace)
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import requests
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
image = Image.open("cat.jpg")
texts = ["a cat", "a dog"]
inputs = processor(text=texts, images=image, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits_per_image)4. Video Transformers
Just like transformers changed NLP and images, they now work on videos.
A video is simply a sequence of many images (frames).
Video Transformers learn patterns in:
- Movement
- Actions
- Scenes
- Facial expressions
How They Work
- Break the video into frames
- Encode each frame
- Use transformers to connect the frames
- Output an understanding of the video
Applications
- Action recognition (jumping, running)
- Video captioning
- Video summarization
- Security camera analysis
- Sports analytics
Simple Code Example (Video Classification Skeleton)
import torch
import torchvision.models as models
model = models.video.r3d_18(pretrained=True) # simple video model
video_tensor = torch.rand(1, 3, 16, 112, 112) # fake video: 16 frames
output = model(video_tensor)
print(output.shape)5. Why Multimodal Learning Is the Future
A. Closer to Human Intelligence
Humans use multiple senses together.
Multimodal AI does the same.
B. Advanced Real-World Applications
- Autonomous robots
- Chatbots that see and speak
- Medical diagnosis systems
- Fraud detection combining text + video
C. Better Performance
Models become more accurate because they learn from richer data.
6. Summary Table
| Concept | Meaning | Example |
|---|---|---|
| Multimodal learning | Using multiple inputs | Image + caption |
| CLIP | Connects text + image | Caption matching |
| Video Transformers | Understand videos | Action recognition |
| Audio + Text | Voice assistants | Alexa, Siri |
| Three-modality AI | Image + audio + text | YouTube AI |
7. Complete Keras Example (Multimodal Text + Image Classification)
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Embedding, Conv2D, Flatten, Concatenate
from tensorflow.keras.models import Model
# Image branch
image_input = Input(shape=(64, 64, 3))
x1 = Conv2D(16, (3,3), activation='relu')(image_input)
x1 = Flatten()(x1)
# Text branch
text_input = Input(shape=(10,))
x2 = Embedding(input_dim=5000, output_dim=16)(text_input)
x2 = Flatten()(x2)
# Combine branches
combined = Concatenate()([x1, x2])
output = Dense(2, activation='softmax')(combined)
model = Model([image_input, text_input], output)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())