ViT
Transformer that treats an image as a sequence of patches.
Resumen
ViT splits an image into fixed-size patches, embeds each as a token, and processes the sequence with an ordinary transformer. It solves the problem that convolutional architectures bake in assumptions about locality that a large enough model does not need. It exists because it showed that the architecture which won in language transfers to vision once there is enough data, which removed the main reason the two fields had different toolkits.
Grafo de tecnologías
Cómo se conecta ViT con el resto del stack.
Usa y depende de
- Transformer architecture Algorithms ViT based on Transformer High ✓ source
Aplicado a
- Multimodal AI application Multimodal ViT used for Multimodal AI High ✓ source
Alternativas
Comparativas
Comparativas detalladas con las alternativas anteriores.