D&R Blog Vision Transformers Have Already Overtaken CNNs: Here's Why and What's Needed for Best Performance (Ceva's Experts blog - Ronny Vatelmacher, Ceva)

Vision AI Has Moved Beyond CNNs—Now What? Convolutional Neural Networks (CNNs) have long dominated AI vision, powering applications from automotive ADAS to face recognition and surveillance. But the industry has moved on—Vision Transformers (ViTs) are now recognized as the superior approach for many computer vision tasks. Their ability to understand global context, resist adversarial attacks, and analyze complex scenes has made them the new standard in vision AI. The conversation is no longer about whether ViTs will overtake CNNs. They already have. Now, the real challenge is ensuring ViTs run efficiently on hardware designed for their needs. This article will explore why ViTs have become the preferred choice, what makes them different, and what hardware capabilities are essential for maximizing their performance. Why Have Vision Transformers Taken Over? CNNs process images bottom-up, detecting edges and features progressively until a full object is classified. This works well for clean, ideal images, but struggles with occlusions, image corruption, and adversarial noise. Vision Transformers, on the other hand, analyze an image more holistically, understanding relationships between different regions through an attention mechanism. A great analogy, as noted in Quanta Magazine: “If a CNN’s approach is like starting at a single pixel and zooming out, a transformer slowly brings the whole fuzzy image into focus.”View the full article HERE