Vision Transformer (ViT)
An adaptation of the transformer architecture for computer vision that processes images as sequences of patches, achieving state-of-the-art results on image classification and other visual tasks.
Vision Transformers, introduced by Google in 2020, apply the same self-attention mechanism used in LLMs to image understanding. ViTs split images into fixed-size patches (typically 16x16 pixels), flatten them into sequences, and process them with standard transformer layers. When pre-trained on large datasets, ViTs have surpassed CNNs on most vision benchmarks. Variants like DINOv2, Segment Anything (SAM), and CLIP have extended the approach to self-supervised learning, segmentation, and multi-modal understanding.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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