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
An Illustrative Multimodal Task: Generating Image Descriptions
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- Show and Tell: A Neural Image Caption Generator, Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2015.7298935 - This paper introduces a seminal end-to-end neural network model for image captioning, combining a convolutional neural network (CNN) for visual feature extraction with a recurrent neural network (RNN) for sentence generation.
- Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering, Peter Anderson, Xiaodong He, Lei Zhang, Jianfeng Gao, 2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2018.00696 - This work significantly advanced image captioning by proposing a novel attention mechanism that combines bottom-up (object-level) and top-down (contextual) visual features, leading to more accurate and detailed descriptions.