Hey fellow AI enthusiasts! I’m excited to share my first Medium article, where I dive into the inner workings of BLIP-2, a transformer-based model that enables machines to ‘see’ and understand images.
In this article, I take you on a journey through the process of image transformation, from the frozen Vision Transformer (ViT) to the Q-Former that distills patch embeddings into meaningful queries. These queries are then sent to a Large Language Model (LLM) for tasks like image captioning and question answering.
My goal is to provide a clear, tensor-by-tensor explanation of this complex process, without any fluff or jargon. I want to help you understand how these models work, so you can build upon this knowledge and create something amazing.
If you’re familiar with Transformers and want a deeper understanding of image understanding, this article is for you. I’d love to hear your thoughts, feedback, and suggestions on how I can improve. And if you enjoy the article, please leave some claps!