This repository provides a structured learning path for Vision-Language Models (VLMs), targeting individuals seeking to understand the evolution from NLP and Computer Vision fundamentals to state-of-the-art VLM architectures. It aims to offer a comprehensive educational resource for researchers and practitioners in the field.

How It Works

The series progresses through foundational concepts in Natural Language Processing (NLP) and Computer Vision (CV), referencing seminal papers like Word2Vec, Seq2Seq, Attention, BERT, AlexNet, and ResNet. It then moves to early Vision-Language Models such as Show and Tell and Show, Attend and Tell, before covering scaling techniques like LoRA and QLoRA, and finally exploring modern VLMs like Flamingo, LLaVA, BLIP-2, and PaliGemma.

Quick Start & Requirements

• The project is structured as a learning series with tutorials and Colab notebooks.
• Prerequisites include foundational knowledge in NLP and Computer Vision. Specific code dependencies are not detailed in the README.
• The series is scheduled for release in January 2025.

Highlighted Details

• Comprehensive coverage of key papers and models in NLP, CV, and VLMs.
• Structured learning path from fundamentals to advanced topics.
• Includes references to papers on scaling laws and efficient fine-tuning techniques.
• Covers a wide range of modern VLM architectures.

Maintenance & Community

• The project encourages community contributions for suggesting additional papers, models, or techniques.
• No specific community channels or contributor information are provided in the README.

Licensing & Compatibility

• The licensing information is not specified in the README.
• Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project is a planned learning series with content scheduled for release in January 2025, meaning the actual tutorials and code are not yet available. The README does not specify the exact technical stack or implementation details for the tutorials.