{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/vision-transformers",
  "headline": "Vision Transformers: ViT, DINOv2, and the End of CNNs",
  "description": "Vision Transformers (ViTs) have largely replaced CNNs as the dominant architecture in computer vision. DINOv2 demonstrated that self-supervised ViTs produce universal visual features, while SAM 2 extends segmentation to video.",
  "dateCreated": "2026-05-24T02:49:13.673Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)",
      "sameAs": "https://arxiv.org/abs/2010.11929"
    },
    {
      "@type": "CreativeWork",
      "name": "DINOv2: Learning Robust Visual Features without Supervision",
      "sameAs": "https://arxiv.org/abs/2304.07193"
    }
  ]
}