Blogs / Vision Transformers (ViT): The New Frontier in Machine Vision Understanding

Vision Transformers (ViT): The New Frontier in Machine Vision Understanding

Vision Transformers (ViT): پیشتازی جدید در درک بینایی ماشینی

Introduction

In the world of artificial intelligence, there are fundamental questions whose answers reshape the history of technology. One of these pivotal questions was whether architectures designed for language processing could also work for understanding images. This question, asked by researchers at Google Research and Ludwig Maximilian University of Munich in 2020, opened a new chapter in the history of computer vision research, resulting in Vision Transformers or ViT.
For a decade before this breakthrough, Convolutional Neural Networks (CNN) dominated the landscape of computer vision. While these architectures were successful, they provided a fundamentally limited approach to image understanding. Imagine trying to understand a painting by looking through a series of increasingly larger windows. This is essentially how CNNs operate—they start with small details and gradually progress toward larger features. However, this method can lose sight of the bigger picture and often struggles to understand how different parts of an image relate to each other.

The Origins and Evolution of Vision Transformers

The history of Vision Transformers stems from the immense success of transformer architectures in natural language processing (NLP). When transformers were introduced in 2017 through the landmark paper "Attention is All You Need," this architecture fundamentally changed how we process sequential data. They demonstrated an unprecedented ability to understand relationships between words in a sentence, regardless of the distance separating them.
For several years, this breakthrough remained largely confined to language processing. Then, researchers asked a compelling question: "What if we could apply this attention-based approach to images as well?" This inquiry led to the publication of the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale."
The key insight was elegant in its simplicity: instead of processing words, Vision Transformer divides an image into a series of patches or "tokens"—each approximately 16×16 pixels in size. Each patch acts like a "visual word" in the transformer's vocabulary. Just as a language transformer can understand how a negation at the beginning of a sentence affects the meaning of words at the end, ViT can understand how an object in one corner of an image relates to objects in other corners.
This approach represented a fundamental departure from the CNN-dominated paradigm that had controlled computer vision for nearly a decade. While CNNs excel at capturing local patterns through their hierarchical convolutional structure, ViTs offer a fundamentally different way of understanding images—one based on global relationships and attention mechanisms.

How Vision Transformers Work

Image Patch Processing

Imagine you're working on a jigsaw puzzle. Rather than examining each piece in isolation, you constantly compare pieces to understand how they might fit together. You look for patterns that continue from one piece to another, colors that match, and shapes that complement each other.
Vision Transformers operate in a remarkably similar way. When presented with an image, they divide it into patches—typically around 16×16 pixels each. These patches function like puzzle pieces. Unlike traditional CNNs that process each patch somewhat independently initially, Vision Transformers immediately begin examining how each patch relates to all other patches in the image.
Each patch is converted into a sequence of numbers (a vector) that represents its content—think of it as writing a detailed description of each puzzle piece. But here's where it becomes interesting: the transformer also adds information about where each patch is located in the original image. This is like numbering your puzzle pieces to remember their original positions. This combination of content and position information is crucial—it helps the transformer understand both what it's looking at and where everything is in relation to other elements.

The Self-Attention Mechanism

The real magic of Vision Transformers happens in what's called the self-attention mechanism. This is where the transformer learns to focus on the most important relationships between different parts of the image. It's like being at a crowded party—while you can hear many conversations happening around you, you focus your attention on the most relevant ones.
In the context of an image, self-attention allows the transformer to dynamically decide which patches should "pay attention" to which other patches. For example, when identifying a face, the system learns that patches containing an eye should pay special attention to patches that might contain another eye, or patches that might contain a nose or mouth. This ability to create dynamic, content-dependent relationships between different parts of the image is what makes Vision Transformers so powerful.
Consider a practical example: identifying a person playing basketball. A Vision Transformer doesn't merely recognize the person and ball as separate entities—it can understand how they relate to each other. The position of the arms might influence how it interprets the ball's position, and vice versa. This holistic understanding leads to more robust recognition, especially in complex scenes where context matters significantly.
But perhaps the most remarkable aspect of this attention mechanism is its flexibility. Unlike CNNs, which have fixed patterns for combining information from nearby pixels, Vision Transformers can adapt their attention patterns based on the content of each specific image. It's like having a detective who can dynamically change their investigation strategy based on the specific clues they find, rather than following the same procedure every time.

The Training Process of Vision Transformers

How Vision Transformers Learn

The way Vision Transformers learn is fascinating and in many ways mirrors how humans develop visual expertise. Just as a child needs to see many examples of cats to reliably identify them in different contexts, Vision Transformers require extensive training data to develop robust visual understanding. However, the way they learn from this data is unique.
Imagine teaching someone to identify birds. You wouldn't start by giving them a detailed manual of every feather pattern and beak shape. Instead, you'd show them many examples of different birds, allowing them to naturally learn to pick up on important features and patterns. Vision Transformers learn in a similar way, but with an interesting twist: they learn what to pay attention to entirely from the data itself.
The training process begins with what's called pre-training. During this phase, the transformer is shown millions of images and asked to solve a seemingly simple task: looking at a partial image and trying to predict the missing parts. It's like solving countless jigsaw puzzles where some pieces are hidden. Through this process, the transformer learns to understand the fundamental patterns and relationships that make up visual scenes.
What makes this approach particularly powerful is that the transformer isn't just memorizing specific images—it's learning general principles about how visual elements relate to each other. Just as a human who's good at jigsaw puzzles can tackle new puzzles they've never seen before, a well-trained Vision Transformer can understand new images by applying the principles it has learned.

Scalability and Efficiency

The Unlimited Scalability of Vision Transformers

One of the most exciting aspects of Vision Transformers is how well they scale with more data and computing power. It's like having a student who not only learns from every example they see but actually gets better at learning as they encounter more examples. Traditional CNNs eventually hit a ceiling where adding more data or making the model larger doesn't help much. Vision Transformers, on the other hand, continue to improve as they scale up.
However, this scalability comes with interesting challenges. Think of it like trying to have a conversation in an increasingly crowded room—the more people (or in our case, image patches) involved, the more difficult it becomes to manage all potential interactions. Researchers have developed clever solutions to this challenge, such as having the transformer focus only on the most important relationships rather than trying to track every possible connection.

Practical Applications of Vision Transformers

Medicine and Diagnosis

Computer vision in medical imaging has been fundamentally transformed by Vision Transformers' ability to understand complex spatial relationships. ViT's capability to analyze relationships between different parts makes it ideal for analyzing X-rays and MRI scans. When analyzing medical images, understanding how different parts relate to each other is crucial. A small anomaly becomes more meaningful when considered in relation to surrounding tissues. Vision Transformers excel at this type of contextual analysis, often catching subtle patterns that traditional methods might miss.

Autonomous Vehicles

In the realm of autonomous vehicles, Vision Transformers are helping vehicles better understand their environment. Traditional systems might recognize individual elements like cars, pedestrians, and traffic signs separately. But Vision Transformers can understand how these elements interact with each other—for example, how a pedestrian's position and movement relate to nearby vehicles and traffic signals. This holistic understanding leads to better predictions of how different elements in the scene might behave.

Image Processing and Photo Organization

Even in everyday applications like photo organization and editing, Vision Transformers make an impact. They can understand the content and context of photos, enabling systems to organize photos based on what they depict or even provide recommendations for better editing.

Comparing Vision Transformers with Traditional CNNs

Which One is Better?

This is a question that has puzzled many researchers and professionals. The reality is that both approaches have advantages and disadvantages:
Vision Transformers:
  • ✓ Better understanding of global relationships in images
  • ✓ Excellent scalability with larger datasets
  • ✗ Require very large amounts of training data
  • ✗ Heavy computational demands compared to simple CNNs
  • ✓ Require fewer training data
  • ✓ Faster computations
  • ✗ Weaker understanding of global relationships
  • ✗ Limited in scalability
Best Approach: Many modern systems use a combination of both—CNNs for initial features and Vision Transformers for understanding global relationships.

Related Technologies and New Architectures

Multimodal Transformers

Multimodal models process images and text simultaneously. These models can look at a photo and write descriptions of it, or read text and generate related images.

ViT and Deep Learning

Deep learning ViT uses multiple layers of abstract representations. Each layer learns more complex patterns than the previous one.

Attention Mechanism in ViT

The attention mechanism is the heart and soul of Vision Transformers. This mechanism decides which parts of the image are most important for completing a task.

Generative Models and ViT

Generative models like Generative Adversarial Networks (GANs) and diffusion models can also use Vision Transformers to generate new images.

Choosing a Framework for Vision Transformers

If you want to use Vision Transformers in your projects, several excellent options are available:

PyTorch

PyTorch is one of the most popular deep learning frameworks. This framework provides excellent capabilities for implementing Vision Transformers.

TensorFlow

TensorFlow is another option that includes Keras, high-level frameworks that make building complex models easier.

OpenCV

OpenCV is a powerful tool for image processing that's used to prepare image data before feeding it to Vision Transformers.

Challenges and Limitations of Vision Transformers

High Computational Demand

The most significant challenge facing Vision Transformers is that they require extensive computational resources. Unlike lightweight CNNs, Vision Transformers are typically large and require powerful GPUs or TPUs for training.

Need for Large Amounts of Data

Vision Transformers require a huge number of training images to work correctly. Unlike CNNs that can learn from less data, Vision Transformers without pre-training on large datasets don't produce good results.

Interpretability

Like many deep artificial intelligence models, Vision Transformers are black-box models—understanding exactly why a specific decision was made can be challenging.

Results and Practical Performance

Real-World Efficiency

Multiple studies have shown that Vision Transformers, when given sufficient training data, produce extraordinary results. On standard image recognition tasks like ImageNet, ViTs typically match or exceed the performance of advanced CNNs.

Pre-trained Models and Practical Use

Fortunately, you don't need to train Vision Transformers from scratch. Pre-trained models from research centers like Google are available for transfer learning. This makes it possible to leverage the power of ViT even with limited data.

The Future of Vision Transformers

Upcoming Developments and Current Research

The world of Vision Transformers is rapidly evolving. Researchers are continuously working on improving efficiency, reducing computational requirements, and discovering new applications.

Combining with Other Technologies

One fascinating aspect is the combination of Vision Transformers with other AI technologies. For example, combining ViT with Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM) can be useful for time-series forecasting tasks with images.

ViT and Large Language Models

The combination of Large Language Models (LLM) with Vision Transformers has resulted in the creation of multimodal models that can comprehensively understand and generate both images and text. These models are used for tasks like automatically creating image captions and answering questions about image content.

Ethics and Security Issues

Bias and Fairness in Vision Transformers

Like any artificial intelligence system, Vision Transformers can amplify biases present in training data. If a model is trained on images with unbalanced representation, results might be unfavorably skewed for underrepresented groups.

Security and Generating False Content

Deepfakes and artificially generated content pose serious risks. Vision Transformers, just like other image generation models, can be used to create false and misleading images.

Privacy Concerns

Using Vision Transformers for facial recognition and person identification raises serious privacy concerns. Ethical and legal responsibility in using these technologies is paramount.

Conclusion

Vision Transformers represent a revolutionary breakthrough in computer vision that demonstrates how the biggest advances in artificial intelligence often come from questioning old assumptions and trying new approaches. The history of ViT—from the success of transformers in NLP to their application in computer vision—shows that innovation often begins by applying existing ideas to new problems.
Although Vision Transformers currently face significant challenges—such as the need for heavy computation and large volumes of data—their advantages are clear. Superior understanding of global relationships in images, better scalability, and diverse applications in medicine, automotive, and many other fields make them essential tools for the future of computer vision.
The future of Vision Transformers is bright. With continued research, improved algorithms, and increasing computational efficiency, it's expected that Vision Transformers will gradually become more accessible for increasingly diverse applications. The computer vision revolution is just beginning.