The Problem
Before 2020, computer vision meant convolutions. Every SOTA on ImageNet, COCO, and detection benchmarks used a CNN backbone. Transformers were for language.
Dosovitskiy et al. (2020) — "An Image is Worth 16x16 Words" — showed you can drop the convolutions entirely. Slice an image into fixed-size patches, linearly project each patch into an embedding, feed the sequence to a vanilla transformer encoder. At sufficient scale (ImageNet-21k pretraining or bigger), ViT matches or beats ResNet-based models.
ViT was the start of a broader pattern in 2026: one architecture, many modalities. Whisper tokenizes audio. ViT tokenizes images. Action tokens for robotics. Pixel tokens for video. The transformer doesn't care — feed it a sequence and it learns.
By 2026, ViT and its descendants (DeiT, Swin, DINOv2, ViT-22B, SAM 3) own most of vision. CNNs still win on edge devices and latency-sensitive tasks. Everything else has a ViT somewhere in the stack.
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