De-mystifying Multimodal Learning: Enabiling Vision in Language Models

In the article "De-mystifying Multimodal Learning: Enabling Vision in Language Models," Matteo Nulli explores the integration of vision into language models through Vision Language Models (VLMs). The text delves into the mathematical foundations, architectural design, and training processes that align visual and textual data. It describes the transformation of images into language-compatible vectors by using Vision Encoders like ViT-CLIP, which break images into patches, apply linear projections, and use contrastive learning to align image and text features. The VLM pipeline involves processing these visual tokens through a connector, merging them with textual tokens, and inputting the combined data into a Large Language Model (LLM) for interpretation. The article concludes by highlighting the challenge of optimizing visual token count to improve inference efficiency, with a promise to explore this topic further in a subsequent post.