What is ViT - and Why is it a Paradigm Shift in Computer Vision?

In the world of AI, it was long believed that computer vision models should mimic the structure of images: convolutional layers sliding over pixels to detect local patterns. Then came ViT - Vision Transformer - and showed that we can think completely differently.

What is ViT?

ViT is a Transformer model adapted for image processing. Instead of treating an image as a continuous 2D array of pixels, ViT breaks it into a sequence of small “patches,” each represented as a token - just like words in a sentence.

This creates a series of tokens, which the Self-Attention mechanism processes just as it does in textual models.

How Does It Work?

Patch Embedding

The image is divided into small squares - for example, 16×16 pixels. Each square is converted into a vector.

Positional Encoding

Like in a sentence, we need to know the order and origin of each patch. Thus, positional information is added to each patch.

Transformer Layers

Self-Attention allows the model to learn relationships between every patch and every other patch - not just those physically close.

Classification or Other Tasks

After processing, a global representation of the image is obtained, which is used for classification, detection, or more complex understanding.

Why is it So Powerful?

The major advantage of ViT is its ability to view the entire image as a single unit. In convolutional models (CNNs), the view starts locally and gradually builds up to a global structure. ViT, on the other hand, enables immediate connections between all parts of the image - without distance limitations.

This Provides:

• The ability to understand complex structures
• Flexibility for a variety of tasks
• Natural integration with textual models
• Reliance on the proven power of Transformer architectures

The Drawback - and Its Solutions

The basic ViT requires significant computational power because every token “sees” every other token. When the image is large, the number of tokens grows accordingly.

Popular Solutions Include:

• Reducing patch resolution
• Using Hierarchical ViT (e.g., Swin Transformer)
• More efficient Attention mechanisms

Modern models like InternViT rely precisely on such innovations.

The Analogy

ViT can be described as a team of experts not sitting in fixed rows but around a round table:

• Each expert can directly talk to any other.
• Everyone shares information in real-time.
• They understand the “big picture” faster than a hierarchical committee where information passes step-by-step.

This is why ViT excels at recognizing distant patterns and global components.

Architectural Tip

When the tensor enters the ViT model, the patch size dramatically affects performance:

• Small patches → many tokens → detailed understanding but computationally expensive.
• Large patches → fewer tokens → faster processing but lower resolution.

In large inference systems, tuning the patch size to the business need is one of the key ways to maintain low response times.

Conclusion

ViT marks the transition from a local to a global approach in image processing. No more “layered scans,” but direct understanding of the entire image structure. This idea has led to a new generation of vision models - and advanced lineages like InternViT and large multi-modal systems are built upon it.