Introduction to Multimodal AI
Chapter 1: What is Multimodal AI?
Artificial Intelligence: A Brief Overview
Understanding Data Modalities: Text, Images, Audio
Defining Multimodal AI: Processing Diverse Data
Benefits of Combining Multiple Modalities
Multimodal vs. Unimodal AI: Core Differences
Examples of Multimodal Systems
Fundamental Challenges in Multimodal AI
An Illustrative Multimodal Task: Generating Image Descriptions
Practice: Identifying Modalities in Common Technologies
Quiz for Chapter 1
Chapter 2: Data Foundations for Multimodal Systems
Text Data Representation: From Characters to Meaning
Image Data Representation: Pixels, Features, and Structure
Audio Data Representation: Sound Waves to Digital Signals
Video Data: Sequences of Images and Sound
Basic Preprocessing for Different Data Types
Aligning Data from Multiple Sources
Comparing Information Across Modalities
Hands-on Practical: Observing Data Formats
Quiz for Chapter 2
Chapter 3: Techniques for Integrating Modalities
Approaches to Multimodal Fusion: Early, Intermediate, Late
Early Fusion: Combining Data at the Input Stage
Intermediate Fusion: Merging Processed Features
Late Fusion: Combining Independent Predictions
Shared Representations: Learning Common Features
Coordinated Representations: Mapping Between Modalities
Basic Architectures for Multimodal Learning
Introduction to Attention: Focusing on Relevant Information
Practice: Visualizing Fusion Methods
Quiz for Chapter 3
Chapter 4: Components of Multimodal AI Models
Extracting Features from Text Data
Extracting Features from Image Data
Extracting Features from Audio Data
Simple Neural Network Layers for Multimodal Tasks
Measuring Performance: Loss Functions for Combined Data
Training Multimodal Systems: An Overview
Basic Evaluation Metrics for Multimodal Outputs
Hands-on Practical: Conceptualizing a Simple Model
Quiz for Chapter 4
Chapter 5: Introductory Applications of Multimodal AI
Image Captioning Systems: Generating Text from Images
Visual Question Answering: Interacting with Images Through Questions
Text-to-Image Synthesis: Creating Visuals from Descriptions (Introduction)
Speech Recognition Enhanced by Visual Cues (Introduction)
Multimodal Sentiment Analysis: Understanding Opinions from Multiple Cues
Inputs and Outputs in Multimodal Applications
Practice: Brainstorming a Multimodal Solution
Quiz for Chapter 5
Comparing Information Across Modalities
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- Learning Transferable Visual Models From Natural Language Supervision, Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever, 2021 Proceedings of the 38th International Conference on Machine Learning, Vol. 139 (PMLR) DOI: 10.5555/3540306.3540445 - This paper presents CLIP, a model that learns robust shared representations for images and text using contrastive learning, which enables direct cross-modal comparison and shows the utility of shared embedding spaces.
- Efficient Estimation of Word Representations in Vector Space, Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean, 2013 International Conference on Learning Representations (ICLR) Workshop DOI: 10.48550/arXiv.1301.3781 - This work introduces Word2Vec, a method for learning dense vector representations of words where semantic relationships are captured by vector proximity and cosine similarity, illustrating the principles of information comparison.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - Chapter 15, 'Representation Learning,' explains how neural networks learn meaningful data representations, which supports creating the feature vectors and shared spaces used for cross-modal comparison.