Computer Vision Algorithms Led AI — Until Transformers Took Over

Until 2017, most AI advancements were driven by breakthroughs in computer vision, largely powered by Convolutional Neural Networks (CNNs). Models like ResNet, YOLO, and Faster R-CNN enabled significant progress in tasks such as image classification, object detection, and segmentation.

The Turning Point: Transformers in 2017

In 2017, the introduction of the Transformer architecture through the paper "Attention is All You Need" marked a major shift in the AI landscape.

• Originally designed for Natural Language Processing (NLP)
• Led to models like:
  • BERT (Bidirectional Encoder Representations from Transformers)
  • GPT (Generative Pretrained Transformer)
  • T5 (Text-To-Text Transfer Transformer)

These models achieved state-of-the-art performance in many NLP benchmarks and brought language models to the center of AI research.

Transformers Expand Beyond Text

Over time, the impact of Transformers extended beyond NLP:

• Computer Vision:
  • ViT (Vision Transformer)
  • SAM (Segment Anything)
• Multi-modal Models:
  • CLIP (connects text and images)
  • DALL·E (text-to-image generation)

These models demonstrate the flexibility and scalability of the Transformer architecture across vision, language, and beyond.

A Paradigm Shift in AI

The shift from CNN-dominated pipelines to Transformer-based architectures represents one of the most significant transitions in the history of AI.

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