Multimodal Foundation Models Pretraining

Course Content Overview

This course provides a deep dive into the concepts, techniques, and workflows involved in pretraining and self-supervised learning for foundation models, particularly focusing on multimodal models that combine text and image data. The course is structured to offer both theoretical foundations and practical insights into building and fine-tuning state-of-the-art models. Starting from the basics, we will explore data collection, proxy tasks, self-supervised learning methods, and text-image alignment techniques. We will also cover cutting-edge topics such as contrastive learning, image tokenization, and unified image-text modeling.

By the end of this course, students will have a comprehensive understanding of pretraining techniques, particularly in the context of large multimodal models that bridge the gap between images and text. They will also gain hands-on knowledge of current advancements in model architectures and the tools needed to work with multimodal data.

What Will Students Learn?

Section 1: Introduction

• Lecture 1: Why Do We Need Pretraining?

  • Understand the role of pretraining in the development of robust foundation models.

  • Learn how pretraining with large-scale datasets helps models generalize across tasks.

  • Discuss why it’s essential for improving performance in transfer learning.

• Lecture 2: What is a Foundation Model?

  • Explore the concept of foundation models and their importance in AI development.

  • Learn how foundation models serve as the backbone for various downstream tasks.

Section 2: General Workflow and Specification

• Lecture 3: Collecting Noisy Data

  • Gain an understanding of noisy data and its role in model pretraining.

  • Learn strategies for collecting diverse, large-scale datasets and managing data quality.

• Lecture 4: Key Components and Architectures

  • Study the critical components that make up large-scale models.

  • Understand the architecture of foundational models and how they differ from task-specific models.

• Lecture 5: Text-Image Feature Alignment

  • Learn the importance of aligning text and image features for multimodal learning.

  • Explore different strategies for effective feature alignment between text and image data.

Section 3: Pretraining With Proxy Tasks

• Lecture 6: Overview of Proxy Tasks

  • Understand what proxy tasks are and their relevance in pretraining.

  • Learn how proxy tasks help in the training of models without task-specific labels.

• Lecture 7: Some Well-Known Proxy Tasks

  • Study popular proxy tasks like predicting missing words or image parts and their impact on model performance.

  • Understand how proxy tasks bridge the gap between unsupervised and supervised learning.

• Lecture 8: What is Missing in General Proxy Tasks?

  • Discuss the limitations of general proxy tasks in real-world applications.

  • Explore possible ways to extend proxy tasks for more sophisticated pretraining.

Section 4: Image-Only Self-Supervision I: Contrastive Learning

• Lecture 9: Overview of Contrastive Learning

  • Understand the principles behind contrastive learning and how it helps build representations for images.

  • Learn how positive and negative pairs are used to improve the model’s ability to differentiate between features.

• Lecture 10: SimCLR

  • Dive deep into the SimCLR framework and understand how it uses contrastive loss for self-supervised learning.

  • Explore its implementation and applications in image classification.

• Lecture 11: BYOL

  • Learn about the BYOL (Bootstrap Your Own Latent) approach, which avoids negative pairs while still achieving state-of-the-art results.

  • Compare the differences between BYOL and other contrastive methods like SimCLR.

• Lecture 12: DINO

  • Study DINO (Distillation with No Labels), a self-supervised method that combines knowledge distillation and contrastive learning.

  • Understand its use in learning better image representations without relying on labeled data.

Section 5: Image-Only Self-Supervised Learning II: Image Tokenization

• Lecture 13: VQ-VAE

  • Understand how Vector Quantized Variational Autoencoders (VQ-VAE) perform image tokenization.

  • Learn about the encoding-decoding process and how it facilitates better image representation learning.

Section 6: Image-Only Self-Supervised Learning III: Masked Image Modeling

• Lecture 14: Mask-then-Predict (MAE, MaskFeat)

  • Learn about masked image modeling techniques, including MAE (Masked Autoencoders) and MaskFeat.

  • Study how these models predict missing parts of an image to learn useful representations.

• Lecture 15: High-Level Features as Targets (BEIT, PeCo)

  • Explore how models like BEIT (BERT Pretraining for Image Transformers) and PeCo (Pretraining Contrastive Features) leverage high-level feature representations as targets.

• Lecture 16: High-Level Features as Targets (SplitMask, PeCo)

  • Understand how methods like SplitMask further improve the use of high-level features and their applications in image pretraining.

Section 7: Text-Image Alignment I: CLIP and Its Variants

• Lecture 17: Basics of CLIP Training

  • Understand how CLIP (Contrastive Language-Image Pretraining) trains a multimodal model using text and image pairs.

  • Learn the foundations of CLIP and how it aligns visual and textual data.

• Lecture 18: What Does CLIP Actually Learn?

  • Explore the inner workings of CLIP and the kind of representations it learns.

  • Learn how CLIP interprets both images and text in a shared space.

• Lecture 19: CLIP Improvements (STAIR, FLIP, LaCLIP)

  • Study various improvements on the original CLIP architecture such as STAIR, FLIP, and LaCLIP, and their effects on performance.

• Lecture 20: Extending CLIP to Multimodality

  • Learn how CLIP can be adapted to other modalities beyond image and text, including audio and video.

Section 8: Text-Image Alignment II: Fine-Grained Correspondences

• Lecture 21: Overview of Fine-Grained Alignment

  • Study how fine-grained alignment between text and image improves model accuracy in image-text tasks.

• Lecture 22: Enhancing Text-to-Image Alignment (ViLD, RegionCLIP)

  • Explore how techniques like ViLD and RegionCLIP enhance the alignment of visual and textual features.

• Lecture 23: UNITER

  • Learn about UNITER, a unified image-text representation model, and its ability to model joint representations.

• Lecture 24: Downstream Tasks

  • Explore how well-aligned text and image models are applied to downstream tasks such as image captioning and visual question answering.

• Lecture 25: Better Input Representation (VILLA, OSCAR)

  • Study the VILLA and OSCAR methods for improving input representations in multimodal models.

• Lecture 26: Refined Image Decomposition (PixelBERT, ViLT)

  • Understand how refined image decomposition methods like PixelBERT and ViLT help in extracting more detailed image features.

Section 9: Text-Image Alignment III: Unified Image-Text Modeling

• Lecture 27: Overview of Unified Image-Text Modeling

  • Learn about unified models that combine text and image inputs into a single framework, enhancing their capabilities.

• Lecture 28: Image Captioning (SimVLM, CoCa)

  • Study image captioning models like SimVLM and CoCa, which generate textual descriptions from images.

• Lecture 29: VL-T5/-BART

  • Explore how the VL-T5 and VL-BART models utilize transformers for image-text tasks, particularly for captioning and visual reasoning.

• Lecture 30: Pretraining with Frozen Encoders (BLIP-2, Flamingo)

  • Learn about pretraining strategies that use frozen encoders, as implemented in models like BLIP-2 and Flamingo.

Summary of Learning Outcomes

By the end of this course, students will:

1. Understand the foundational concepts of pretraining, self-supervised learning, and foundation models.

2. Gain hands-on knowledge of data collection strategies, architectures, and text-image feature alignment techniques.

3. Master a range of self-supervised learning methods, including contrastive learning, image tokenization, and masked image modeling.

4. Learn how models like CLIP and its variants align and process multimodal data, as well as techniques for improving fine-grained alignment.

5. Become proficient in cutting-edge models for unified image-text tasks such as image captioning, visual question answering, and multimodal representation learning.

6. Acquire the skills necessary to implement and fine-tune pretraining strategies on multimodal data for advanced AI applications.

This course prepares students to build, evaluate, and improve large-scale multimodal models in a variety of practical scenarios.