03_vision_language_models

Vision Language Models (VLMs)

Introduction

Vision Language Models (VLMs) are AI systems that can understand and process both visual and textual information. They bridge the gap between computer vision and natural language processing, enabling AI to comprehend and describe visual content.

Topics Covered

1. Algorithms

• Contrastive Learning
• Masking-based VLMs
• Generative-based VLMs
• Pretrained Backbone-based VLMs

2. Architectures

• Vision Transformer (ViT)
• Dual Encoder
• Fusion Encoder-Decoder

3. Training Techniques

• Data Collection and Preprocessing
• Data Pruning
• Contrastive Learning
• Masked Language-Image Modeling
• Transfer Learning

4. Ethical Considerations

• Bias Mitigation
• Responsible AI Development
• Fairness Evaluation

5. Tools and Libraries

• Hugging Face Transformers
• NVIDIA NeMo

Learning Resources

Documentation and Guides

• Vision Language Model Prompt Engineering Guide
• Hugging Face Transformers Documentation
• NVIDIA NeMo Documentation

Articles and Tutorials

• Vision Language Models (VLMs) Explained
• A Deep Dive into VLMs: Vision-Language Models
• What is a Vision-Language Model (VLM)?
• Guide to Vision-Language Models (VLMs)