The Rise of Transformers in Vision and Multimodal Models – Hugging Face – day 72

The Rise of Transformers in Vision and Multimodal Models In this first part of our blog series, we’ll explore how transformers, originally created for Natural Language Processing (NLP), have expanded into Computer Vision (CV) and even multimodal tasks, handling text, images, and video in a unified way. This will set the stage for Part 2, where we will dive into using Hugging Face and code examples for practical implementations. 1. The Journey of Transformers from NLP to Vision The introduction of transformers in 2017 revolutionized NLP, but researchers soon realized their potential for tasks beyond just text. Originally used alongside Convolutional Neural Networks (CNNs), transformers were able to handle image captioning tasks by replacing older architectures like Recurrent Neural Networks (RNNs). How Transformers Replace RNNs Transformers replaced RNNs due to their ability to capture long-term dependencies and work in parallel rather than sequentially, like RNNs. This made transformers faster and more efficient, especially for image-based tasks where multiple features needed to be processed simultaneously. 2. The Emergence of Vision Transformers (ViT) In 2020, researchers at Google proposed a completely transformer-based model for vision tasks, named the Vision Transformer (ViT). ViT treats an image in a way similar to text data—by…