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
Basic Evaluation Metrics for Multimodal Outputs
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- BLEU: a Method for Automatic Evaluation of Machine Translation, Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu, 2002 Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics) DOI: 10.3115/1073083.1073135 - Introduces the widely used BLEU metric for evaluating machine-generated text, a core component for assessing image captioning models.
- CIDEr: Consensus-based Image Description Evaluation, Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh, 2015 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) DOI: 10.1109/CVPR.2015.7298926 - Presents CIDEr, a metric specifically designed for evaluating image captions by measuring consensus with human descriptions, directly relevant to the section.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - A foundational textbook covering various aspects of deep learning, including explanations of common classification evaluation metrics like accuracy, precision, recall, and F1-score.
- VQA: Visual Question Answering, Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, 2015 Proceedings of the IEEE International Conference on Computer Vision (ICCV) (IEEE) DOI: 10.1109/ICCV.2015.279 - Introduces the benchmark VQA dataset and task, providing context for the evaluation of VQA models.