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
Extracting Features from Image Data
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- Computer Vision: Algorithms and Applications, Richard Szeliski, 2022 (Springer) - A widely-recognized textbook covering classical computer vision algorithms, including detailed discussions on color histograms, edge detection (e.g., Canny), and various hand-crafted image feature extraction methods.
- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, 2012 Advances in Neural Information Processing Systems, Vol. 25 (Curran Associates, Inc.) DOI: 10.55988/NIPS.2012.2155 - A seminal paper that introduced AlexNet, a deep convolutional neural network that significantly advanced the state-of-the-art in image classification on the ImageNet dataset, demonstrating the efficacy of learned features from deep networks.
- ImageNet: A Large-Scale Hierarchical Image Database, Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009) (IEEE) DOI: 10.1109/CVPR.2009.5206848 - This paper describes the ImageNet dataset, which has become a crucial benchmark for training and evaluating large-scale deep learning models, enabling the development of powerful pre-trained CNNs used as feature extractors.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A widely-cited textbook offering an in-depth theoretical and practical explanation of deep learning, including comprehensive chapters on convolutional networks, representation learning, and how deep models extract features.