Understanding the Vision Transformer: foundations and applications
While the convolutional neural networks (CNN) have long dominated image processing, Vision Transformer (or “Vision Transformer”) is emerging to offer an innovative approach in the field of artificial intelligence. It should be remembered that data labeling by experts is important to maximize the accuracy and effectiveness of AI models. At the crossroads between advances in natural language processing and computer vision, this technology is based on the foundations of transformers.
As a reminder, in AI, transformers propose an architecture that has revolutionized the processing of sequential data such as text. By applying the principles of transformers to the visual domain, the vision transformer defies established conventions by replacing CNN network operations with self-attention mechanisms. In short, we explain everything to you!
What is a Vision Transformer?
A Vision Transformer is a neural network architecture for processing data such as images, inspired by the transformers used in natural language processing. Unlike traditional convolutional neural networks (or CNNs), it uses self-attention mechanisms to analyze relationships between parts of the image.
By dividing the image into patches and applying self-attention operations, it captures spatial and semantic interactions. This allows for a global representation of the image. With layers of self-attention and transformation Feed-Forward, it learns hierarchical visual characteristics.
This approach opens up new perspectives in object recognition, the image segmentation..., in the field of computer vision. The results obtained through the use of Vision Transformers are remarkable in terms of efficiency and precision.
How do vision transformers work?
We insist (so that you remember this principle): the Vision Transformer works by dividing an image into patches, then treating these patches as data sequences. Each patch is represented by a vector, then each pair of vectors is evaluated for their relationships using self-attention mechanisms.
These mechanisms allow the model to capture the spatial and semantic interactions between the patches, focusing on the relevant parts of the image. Then, this information is propagated through several transformation layers Feed-Forward, allowing the model to learn hierarchical and abstract representations of the image.
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What is the origin of the Vision Transformer?
The Vision Transformer (or ViT) was originally developed for natural language processing and then applied to computer vision. It was first introduced in an article entitled”An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale“by Alexey Dosovitskiy et al., published in 2020. It is therefore (relatively) recent!
The fundamental idea behind ViT is to process images as sequences of “patches” (or pieces) rather than individual pixels. These patches are then processed by a Transformer model, which is capable of capturing the long-distance dependencies between the various elements of the sequence.
What are the influences of ViT in the field of AI?
The innovative architecture of the Vision Transformer (ViT) merges the concepts of convolutional neural networks and Transformer models. Its influences are multiple and include:
Transformers in NLP
The main influence comes from the Transformers models that revolutionized natural language processing. Attention mechanisms have been particularly effective in improving the comprehension of sentences in English and their translation into French. Models like BERT, GPT, and others have demonstrated the effectiveness of attention mechanisms in capturing sequential relationships.
Convolutional neural networks (CNN)
Although ViT uses a Transformer architecture, its initial field of application is heavily influenced by CNNs, which have long dominated AI developments in this field (and are still being used successfully, by the way). These are great for capture patterns locals in an image, and ViT takes advantage of this knowledge by dividing the image into patches.
Attention mechanism & self-attention
The attention mechanism is a key component of Transformers. It allows the model to weight different parts of the input data, according to their importance for a given task. For example, this mechanism makes it possible to determine the importance of each word in relation to the others in the context of a sentence. This idea has been successfully extended to image data processing in ViT.
The concept of self-attention, where each element of a sequence (or an image, in the case of ViT) can interact with all the other elements, is fundamental for Transformers and therefore for ViT. This allows the model to capture contextual dependencies, thus improving “understanding” by the model and data generation.
How does the Vision Transformer differ from other image processing architectures?
The Vision Transformer differs from other image data processing architectures in several ways:
Using Transformers
Unlike conventional image processing architectures that are mainly based on convolutional neural networks (CNN), ViT applies Transformers mechanisms. This approach allows ViT to capture long-distance relationships between different image elements more effectively.
Image patch processing
Instead of processing each pixel individually, ViT divides the image into patches (or pieces) and processes them as a data sequence. This allows the model to handle images of varying sizes without the need for convolutions specific to the size of the image.
Global self-attention
Unlike CNNs that use convolutional operations to extract local characteristics, ViT uses global self-attention mechanisms that allow each element of the image to interact with all the others. This allows the model to capture long-distance relationships and complex patterns in the image.
Scalability
ViT is highly scalable, which means it can be trained on large amounts of data and adapted to different image sizes without requiring major changes to its architecture. This makes it a versatile architecture that is adaptable to a variety of computer vision tasks.
What are the typical use cases for the Vision Transformer?
The vision transformer (ViT) has shown its effectiveness in various computer vision use cases.
Image classification
ViT can be used for image classification, where he is trained to recognize and classify different objects, scenes, or image categories. It has demonstrated performances comparable to, or even superior to, those of traditional CNN architectures in this task.
Object detection
Although CNNs have traditionally dominated object detection, the ViT is also able to handle this task successfully. By using techniques such as multi-scale object detection and the integration of self-attention mechanisms, ViT can effectively detect and locate objects in an image.
Semantic segmentation
ViT can be used for semantic segmentation, where the objective is to assign a semantic label to each pixel in the image. By exploiting the self-attention skills of ViT, it is possible to capture the spatial relationships between the various elements of the image and to perform precise segmentation.
Recognition of actions
ViT can be used for Recognizing actions in videos, where the objective is to recognize and classify the various human actions or activities present in a video sequence. By using temporal modeling techniques and treating each frame in the video as a data sequence, ViT can be adapted to this task.
Image generation
Although less common, ViT can also be used for image generation, where the aim is to generate new, realistic, high-quality images from a text description or sketch. By using conditional generation techniques and exploiting the modeling capabilities of Transformers, ViT can generate more high-quality images in a variety of areas.
In conclusion
The Vision Transformer (ViT) marks a significant advance in the field of computer vision, by exploiting self-attention mechanisms to process images in a more global and contextual manner. Building on the successes of transformers in natural language processing, ViT replaces convolutional operations with self-attention techniques, making it possible to capture richer and more complex spatial and semantic relationships within images.
With varied applications ranging from image classification to semantic segmentation, object detection and action recognition, the Vision Transformer proves its efficiency and versatility. Its innovative and scalable approach offers promising perspectives for numerous computer vision tasks, while challenging the conventions established by traditional convolutional neural networks.
High-quality data labeling services play an important role in optimizing the performance of Vision Transformer models. For example, many startups are exploring partnerships with data annotation companies (such as Innovatiana) to accelerate the development of AI models. By allowing more precise and contextualized analysis of images, these services pave the way for even more advanced innovations in the future, using innovative methods such as Vision Transformers.
