Multimodal Foundation Models Pretraining

From Specialists to General-Purpose Models

What you'll learn
Understand pretraining and self-supervised learning for vision and multimodal models, applying these concepts to real-world AI problems.
Master text-image alignment techniques such as contrastive learning and unified image-text modeling to build effective multimodal models.
Implement self-supervised learning methods like SimCLR, BYOL, and DINO to train models without labeled data, improving performance on large datasets.
Apply pretrained models like CLIP and VL-T5 to real-world tasks such as image captioning and visual question answering for multimodal AI applications.

Requirements
Basic Understanding of Machine Learning: Familiarity with core concepts such as supervised learning, loss functions, and optimization methods.
Programming Skills in Python: Experience with Python programming, including working with libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.
Familiarity with Deep Learning Frameworks: Basic knowledge of deep learning frameworks such as TensorFlow or PyTorch, especially for building and training neural networks.
Interest in AI and Multimodal Models: A keen interest in working with image, text, and multimodal data, and a desire to learn about the latest advancements in AI research.

Description
Course Content OverviewThis 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: IntroductionLecture 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 SpecificationLecture 3: Collecting Noisy DataGain 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 ArchitecturesStudy 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 AlignmentLearn 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 TasksLecture 6: Overview of Proxy TasksUnderstand 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 TasksStudy 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 LearningLecture 9: Overview of Contrastive LearningUnderstand 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: SimCLRDive 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: BYOLLearn 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: DINOStudy 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 TokenizationLecture 13: VQ-VAEUnderstand 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 ModelingLecture 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 VariantsLecture 17: Basics of CLIP TrainingUnderstand 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 MultimodalityLearn how CLIP can be adapted to other modalities beyond image and text, including audio and video.Section 8: Text-Image Alignment II: Fine-Grained CorrespondencesLecture 21: Overview of Fine-Grained AlignmentStudy 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: UNITERLearn about UNITER, a unified image-text representation model, and its ability to model joint representations.Lecture 24: Downstream TasksExplore 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 ModelingLecture 27: Overview of Unified Image-Text ModelingLearn 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/-BARTExplore 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 OutcomesBy the end of this course, students will:Understand the foundational concepts of pretraining, self-supervised learning, and foundation models.Gain hands-on knowledge of data collection strategies, architectures, and text-image feature alignment techniques.Master a range of self-supervised learning methods, including contrastive learning, image tokenization, and masked image modeling.Learn how models like CLIP and its variants align and process multimodal data, as well as techniques for improving fine-grained alignment.Become proficient in cutting-edge models for unified image-text tasks such as image captioning, visual question answering, and multimodal representation learning.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.

Aspiring AI Researchers and Engineers: Those looking to deepen their understanding of pretraining methods and work with state-of-the-art multimodal models, including text and image data.,Machine Learning Practitioners: Data scientists and engineers who want to expand their expertise in self-supervised learning techniques and learn how to apply them to large-scale models.,AI Enthusiasts and Students: Beginners with a foundational knowledge of machine learning, who are eager to explore advanced topics like vision-language models and cutting-edge pretraining techniques.,Professionals in Computer Vision and NLP: Developers or researchers working in computer vision, natural language processing, or multimodal applications, looking to upgrade their skills and explore innovative training approaches.