← Back to Blog Vision Transformers (ViTs) Explained: How Transformers Changed Computer Vision

Vision Transformers (ViTs) Explained: How Transformers Changed Computer Vision

πŸ“… 2026-09-19 ⏱ 12 min read ✍ DeepNeuralAI
Vision TransformersViTComputer VisionTransformersDeep Learning
Learn how Vision Transformers (ViTs) use image patches and self-attention to understand images and transform modern computer vision.
Vision Transformers (ViTs) Explained: How Transformers Changed Computer Vision
Vision Transformers (ViTs) Explained: How Transformers Changed Computer Vision

Vision Transformers (ViTs) Explained: How Transformers Changed Computer Vision

For many years, Convolutional Neural Networks (CNNs) were one of the most important architectures in computer vision. They became highly effective at tasks such as image classification, object detection, and image recognition.

Then Transformers changed the way AI processes visual information.

Originally developed for natural language processing, Transformer architectures introduced a powerful mechanism called self-attention. Researchers later adapted this idea for images, leading to Vision Transformers (ViTs).

Instead of processing an image primarily through convolutional filters, a Vision Transformer divides the image into smaller patches and treats those patches somewhat like tokens in a sentence.

The result is a different approach to understanding imagesβ€”one that can model relationships between distant parts of an image using attention.

What Is a Vision Transformer?

A Vision Transformer (ViT) is a deep learning architecture that applies the Transformer architecture to computer vision.

The basic idea is:

Image
   ↓
Split into Patches
   ↓
Patch Embeddings
   ↓
Position Embeddings
   ↓
Transformer Encoder
   ↓
Self-Attention
   ↓
Classification Head
   ↓
Prediction

Instead of directly processing every pixel as an independent input, the image is divided into fixed-size patches.

Each patch is converted into a numerical representation called an embedding.

These embeddings are then processed by Transformer layers.

Why Were Vision Transformers Created?

Traditional CNNs process images using convolution operations.

Convolution is excellent at detecting local patterns such as:

  • Edges
  • Corners
  • Textures
  • Shapes
  • Local features

However, understanding an image sometimes requires relationships between distant regions.

For example, consider an image of a person holding a tennis racket.

The AI may need to understand the relationship between:

  • The person's hands
  • The racket
  • The person's body
  • The surrounding environment

Transformers use self-attention to model relationships between different parts of the input.

This gives Vision Transformers a different way to understand global image context.

CNNs vs Vision Transformers

CNNs and Vision Transformers approach image understanding differently.

CNN

Image
  ↓
Convolution
  ↓
Feature Maps
  ↓
Pooling
  ↓
More Convolution
  ↓
Classification

CNNs progressively extract local and higher-level features.

Vision Transformer

Image
  ↓
Image Patches
  ↓
Patch Embeddings
  ↓
Self-Attention
  ↓
Transformer Layers
  ↓
Classification

ViTs process relationships between image patches using attention.

Neither architecture is simply a universal replacement for the other. The appropriate architecture depends on the task, dataset, hardware, training setup, and deployment requirements.

How Does a Vision Transformer Work?

Let's understand the process step by step.

Step 1: Start With an Image

Suppose we have an RGB image with dimensions:

224 Γ— 224 Γ— 3

The image contains 224 pixels in width, 224 pixels in height, and 3 color channels.

A Vision Transformer does not feed this entire image directly into the Transformer.

Instead, it divides the image into smaller patches.

Step 2: Split the Image Into Patches

Suppose the image is divided into:

16 Γ— 16 pixel patches

A 224 Γ— 224 image can be divided into:

224 / 16 = 14

patches along each dimension.

Therefore:

14 Γ— 14 = 196 patches

Each patch represents a small region of the original image.

Conceptually:

+----+----+----+----+
| P1 | P2 | P3 | P4 |
+----+----+----+----+
| P5 | P6 | P7 | P8 |
+----+----+----+----+
| P9 |P10 |P11 |P12 |
+----+----+----+----+
|P13 |P14 |P15 |P16 |
+----+----+----+----+

A real image contains many more patches, depending on the image and patch size.

Step 3: Flatten the Patches

Each image patch contains pixel values.

For example, a 16 Γ— 16 RGB patch contains:

16 Γ— 16 Γ— 3 = 768

values.

The patch can be flattened into a one-dimensional vector.

Image Patch
     ↓
Pixel Values
     ↓
Flattened Vector

The resulting vector is then transformed into an embedding.

Step 4: Create Patch Embeddings

The flattened patch is passed through a trainable projection layer.

This converts the raw pixel information into a fixed-dimensional vector.

For example:

Patch
  ↓
Flatten
  ↓
Linear Projection
  ↓
Patch Embedding

Each patch becomes a token-like representation.

This is similar to how words or subwords become embeddings in language models.

The important difference is that the tokens represent image patches instead of words.

Step 5: Add Position Information

Transformers do not naturally know where each token came from.

For images, position is important.

For example, a patch from the top-left corner has a different spatial meaning from a patch in the bottom-right corner.

Therefore, Vision Transformers add positional information to the patch embeddings.

Conceptually:

Patch Embedding
       +
Position Embedding
       ↓
Transformer Input

This helps the model understand the spatial arrangement of the image.

Step 6: Add a Class Token

Many Vision Transformer implementations use a special token called the [CLS] token.

The sequence can look like:

[CLS] + Patch 1 + Patch 2 + Patch 3 + ... + Patch N

The class token is trained to collect information from the image through the Transformer layers.

At the end of the network, the representation of this token can be used for classification.

Step 7: Self-Attention

Self-attention is one of the most important components of the Transformer.

It allows the model to determine which parts of the input are important to each other.

Imagine an image containing a dog.

One patch may contain the dog's face.

Another patch may contain its legs.

Another may contain its tail.

Self-attention allows the model to establish relationships between these different patches.

Conceptually:

Patch A ────────┐
                ↓
Patch B β†’ Self-Attention β†’ Global Representation
                ↑
Patch C β”€β”€β”€β”€β”€β”€β”€β”€β”˜

This allows information to flow between different regions of the image.

What Is Self-Attention?

Self-attention uses three main components:

  • Query
  • Key
  • Value

These are commonly represented as:

Q = Query
K = Key
V = Value

The attention mechanism calculates how strongly different tokens should interact.

A simplified version of the attention equation is:

Attention(Q, K, V)
=
softmax(QKα΅€ / √dβ‚–)V

Here:

  • Q represents queries
  • K represents keys
  • V represents values
  • dβ‚– represents the dimension of the key vectors

The result determines how information from different patches should be combined.

You do not need to memorize the equation to understand the main idea:

Self-attention helps the model decide which parts of an image should influence each other.

Multi-Head Self-Attention

Vision Transformers typically use Multi-Head Self-Attention.

Instead of calculating attention only once, the model uses multiple attention heads.

Each head can learn different relationships.

For example, one attention head may focus on:

  • Shape

Another may focus on:

  • Texture

Another may focus on:

  • Spatial relationships

Another may focus on:

  • Object parts

The outputs from the different heads are combined.

              β”Œβ”€ Attention Head 1
              β”œβ”€ Attention Head 2
Input β†’ MHA ──┼─ Attention Head 3
              β”œβ”€ Attention Head 4
              └─ ...
                    ↓
             Combined Features

This allows the model to capture multiple types of relationships simultaneously.

Step 8: Feed-Forward Network

After self-attention, the Transformer applies a feed-forward neural network.

A simplified Transformer block looks like:

Input
  ↓
Multi-Head Self-Attention
  ↓
Add & Normalize
  ↓
Feed-Forward Network
  ↓
Add & Normalize
  ↓
Output

This block is repeated multiple times.

Each layer helps the model build increasingly sophisticated representations.

Step 9: Classification

After the Transformer processes the image patches, the resulting representation is passed to a classification head.

For example:

Transformer
     ↓
Class Token
     ↓
MLP Head
     ↓
Softmax
     ↓
Prediction

The model might produce:

Cat       82%
Dog       12%
Bird       4%
Other      2%

The class with the highest predicted probability becomes the model's output.

Why Is Self-Attention Important for Images?

One of the major ideas behind Vision Transformers is the ability to model relationships across an entire image.

Consider a large image containing a car.

The front of the car might appear in one region while the rear appears somewhere else.

A model needs to understand that these regions belong to the same object.

Self-attention can directly connect information from different patches.

This provides a mechanism for modeling long-range dependencies.

Vision Transformers and Global Context

CNNs build larger receptive fields through layers of convolution and other operations.

Vision Transformers can establish relationships between distant patches through attention.

For example:

Patch 1 ────────────────┐
                        ↓
Patch 25 β†’ Self-Attention β†’ Global Understanding
                        ↑
Patch 100 β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

This global interaction is one of the key concepts behind Vision Transformers.

Vision Transformers vs CNNs

The differences can be summarized conceptually.

FeatureCNNVision TransformerBasic input processingConvolutionImage patchesMain mechanismConvolution filtersSelf-attentionLocal feature extractionStrong inductive biasLearned through attentionGlobal relationshipsBuilt progressivelyDirectly modeled through attentionPosition informationNaturally related to convolution structureUsually added explicitlyTraining requirementsCan work well with smaller datasetsOften benefits from substantial training data or pretrainingArchitectureConvolutional layersTransformer encoder blocksCommon applicationsVision tasksVision and multimodal tasks

These differences do not mean that one architecture is always better for every problem.

What Are the Advantages of Vision Transformers?

Vision Transformers provide several important capabilities.

1. Global Context

Self-attention allows the model to consider relationships between distant image patches.

2. Flexible Architecture

Transformer architectures can be adapted to different vision tasks and model sizes.

3. Strong Scaling

Vision Transformers can benefit significantly from large-scale datasets and computation.

4. Transfer Learning

Pre-trained Vision Transformers can be adapted to specific computer vision tasks.

5. Multimodal Potential

Transformer-based architectures can connect visual information with other modalities such as text.

This has become particularly important for multimodal AI systems.

What Are the Limitations of Vision Transformers?

Vision Transformers also have challenges.

1. Data Requirements

ViTs can benefit from large-scale training datasets and strong pretraining.

When training data is limited, a suitable CNN or hybrid architecture may sometimes be more practical.

2. Computational Cost

Self-attention can become expensive as the number of tokens increases.

Smaller image patches create more tokens, increasing the amount of computation.

3. Memory Usage

Large Transformer models can require substantial GPU memory during training.

4. Training Complexity

Training a high-performance Vision Transformer from scratch can require considerable computing resources.

5. Deployment Considerations

Large ViTs may not be suitable for every mobile or edge device without optimization.

Techniques such as quantization, pruning, distillation, and efficient model architectures can help address these challenges.

What Happens If We Use Smaller Patches?

Patch size has an important effect on a Vision Transformer.

Suppose an image is:

224 Γ— 224

With 16 Γ— 16 patches:

14 Γ— 14 = 196 patches

With 8 Γ— 8 patches:

28 Γ— 28 = 784 patches

Smaller patches provide more detailed information.

However, they also create many more tokens.

More tokens generally mean more computation and memory requirements.

Therefore, patch size is an important design consideration.

Vision Transformers for Image Classification

One of the most common applications of ViTs is image classification.

The workflow looks like:

Image
  ↓
Patch Extraction
  ↓
Patch Embeddings
  ↓
Position Information
  ↓
Transformer Encoder
  ↓
Classification Head
  ↓
Class Prediction

The model can be trained to recognize categories such as:

  • Animals
  • Vehicles
  • Plants
  • Products
  • Objects
  • Medical images

Vision Transformers for Object Detection

Vision Transformers can also be used in object detection systems.

Instead of simply answering:

"What is in this image?"

an object detection system can answer:

"What objects are present, and where are they located?"

For example:

Car
Bounding Box
Confidence Score

Transformer-based detection architectures can use attention to model relationships between objects and image regions.

Vision Transformers for Image Segmentation

Image segmentation requires assigning labels to pixels or regions of an image.

For example, an autonomous system may need to distinguish:

Road
Vehicle
Pedestrian
Building
Sky
Vegetation

Transformer-based architectures can capture broader contextual relationships that are useful for segmentation tasks.

Vision Transformers and Multimodal AI

Transformers have become especially important in multimodal AI.

A multimodal system may process:

  • Text
  • Images
  • Audio
  • Video

For example:

Image + Text
      ↓
Multimodal Model
      ↓
Understanding
      ↓
Answer

This allows AI systems to connect visual information with language.

Vision Transformers can serve as part of the visual processing component in such systems.

Vision Transformers and Transfer Learning

Training a large Vision Transformer from scratch can require substantial data and computing resources.

A common alternative is to start with a pre-trained model.

The workflow can look like:

Pre-trained ViT
      ↓
Fine-Tuning
      ↓
Your Dataset
      ↓
Specialized Vision Model

For example, a pre-trained model can be adapted for:

  • Manufacturing inspection
  • Medical image classification
  • Agriculture
  • Retail products
  • Traffic analysis
  • Satellite imagery

This makes transfer learning particularly useful for practical applications.

Vision Transformers in Real-World Applications

Vision Transformers can be applied across many industries.

Healthcare

Potential applications include medical image analysis, classification, and segmentation.

Autonomous Vehicles

Vision models can help analyze cameras and other visual sensors.

Manufacturing

ViTs can be used for visual inspection and defect detection.

Retail

Computer vision systems can analyze products, shelves, and customer environments.

Agriculture

Vision models can analyze crops, plant conditions, and agricultural imagery.

Security and Monitoring

Computer vision systems can analyze video streams and detect relevant objects or events.

Robotics

Robots can use vision models to understand objects and environments.

Vision Transformers and Edge AI

A major challenge is deploying Transformer models on devices with limited resources.

Edge devices may have:

  • Limited RAM
  • Limited processing power
  • Battery constraints
  • Limited storage

Model optimization can make deployment more practical.

Common techniques include:

  • Quantization
  • Knowledge distillation
  • Pruning
  • Smaller architectures
  • Hardware acceleration

A possible optimization pipeline is:

Large Vision Transformer
          ↓
Knowledge Distillation
          ↓
Smaller ViT
          ↓
Quantization
          ↓
Edge Deployment

Vision Transformers vs Knowledge Distillation

Knowledge distillation can be used to make Vision Transformers more efficient.

A large ViT can act as the teacher.

A smaller vision model becomes the student.

Large ViT
  ↓
Teacher Predictions
  ↓
Student Training
  ↓
Small Vision Model

This can be useful when a large model is accurate but too expensive for the target hardware.

Vision Transformers and Generative AI

Transformer architectures are not limited to classification.

They are also central to many modern generative AI systems.

Vision-focused Transformer architectures can contribute to systems involving:

  • Image understanding
  • Image generation
  • Vision-language models
  • Video understanding
  • Multimodal generation

The broader Transformer architecture has therefore become an important building block across multiple areas of AI.

A Simple Vision Transformer Workflow

The complete concept can be summarized as:

             IMAGE
               ↓
        Split Into Patches
               ↓
         Patch Embeddings
               ↓
        Position Embeddings
               ↓
      Transformer Encoder
               ↓
      Multi-Head Attention
               ↓
      Feed-Forward Network
               ↓
        Class Representation
               ↓
        Classification Head
               ↓
           PREDICTION

This is the core idea behind the Vision Transformer architecture.

Key Takeaways

Vision Transformers changed computer vision by bringing the Transformer architecture and self-attention into image processing.

The most important concepts are:

  • A ViT divides an image into smaller patches.
  • Each patch is converted into an embedding.
  • Positional information helps preserve spatial relationships.
  • Transformer encoders process the patch sequence.
  • Self-attention allows different image regions to interact.
  • Multi-head attention captures different relationships simultaneously.
  • A classification head can use the learned representation to make predictions.
  • ViTs can be used for classification, detection, segmentation, and multimodal AI.
  • Large ViTs can require significant computational resources.
  • Pretraining and transfer learning can make ViTs more practical.
  • Quantization, pruning, and knowledge distillation can help with efficient deployment.

Frequently Asked Questions

What is a Vision Transformer?

A Vision Transformer is a computer vision model that applies the Transformer architecture to images by representing image patches as tokens.

How does a Vision Transformer process an image?

It divides the image into patches, converts those patches into embeddings, adds positional information, and processes them through Transformer encoder layers.

What is the main difference between CNNs and ViTs?

CNNs primarily use convolution operations to extract visual features, while ViTs use self-attention to model relationships between image patches.

Why are image patches used in ViTs?

Transformers are designed to process sequences. Dividing an image into patches converts the visual input into a sequence of patch representations that the Transformer can process.

What is self-attention in Vision Transformers?

Self-attention allows the model to determine how different image patches relate to one another and combine information from those patches.

Are Vision Transformers better than CNNs?

Performance depends on the dataset, architecture, training strategy, computing resources, and application. Both CNNs and Vision Transformers remain useful approaches for computer vision.

Can Vision Transformers be used for object detection?

Yes. Transformer-based architectures can be used for object detection as well as image classification and segmentation.

Can Vision Transformers work on mobile devices?

Yes, but model size and computational requirements matter. Smaller architectures and optimization techniques such as quantization and distillation can help make deployment more practical.

Do Vision Transformers require large datasets?

Large-scale pretraining can be particularly valuable for ViTs. When training data is limited, transfer learning and pre-trained models can help.

Conclusion

Vision Transformers introduced a new way of thinking about computer vision.

Instead of relying primarily on convolutional operations, ViTs divide images into patches and use self-attention to learn relationships between those patches.

The fundamental process is simple:

Image β†’ Patches β†’ Embeddings β†’ Attention β†’ Understanding β†’ Prediction

This approach has expanded the role of Transformers beyond language and into modern computer vision and multimodal AI.

As AI systems increasingly need to understand images, video, and multiple types of data together, Transformer-based vision architectures provide an important foundation for building the next generation of intelligent visual systems.