From CNNs to Vision Transformers: Two Approaches to Computer Vision

Convolutional Neural Networks (CNNs) revolutionized computer vision by learning visual patterns layer by layer—from simple edges to complete objects. Thanks to their computational efficiency and strong performance on relatively small datasets, CNNs remain the foundation of many image classification, object detection, and segmentation systems.

Vision Transformers (ViTs), on the other hand, introduced a fundamentally different approach. Instead of processing images through convolutional filters, they divide an image into smaller patches and use a self-attention mechanism that allows every patch to interact with every other patch. This provides a global understanding of the image from the very beginning of the model's processing pipeline. Given sufficient data and computational resources, ViTs achieve outstanding performance and have driven major advances in object detection, image segmentation, and multimodal models such as CLIP and Stable Diffusion.

The architectural differences between CNNs and ViTs clearly illustrate their complementary strengths. CNNs excel at capturing local patterns efficiently, while Vision Transformers are particularly effective at modeling long-range relationships and global context. Together, these two approaches are shaping the future of computer vision across a wide range of applications—from medical imaging and satellite imagery to next-generation creative AI systems.

Key takeaway: CNNs are specialists in local structures, Vision Transformers excel at understanding global context, and together they are driving the next generation of vision-based artificial intelligence.

CNN explained: https://ravjot03.medium.com/decoding-cnns-a-beginners-guide-to-convolutional-neural-networks-and-their-applications-1a8806cbf536
Vision Transformers: https://cameronrwolfe.substack.com/p/vision-transformers