Whereas convolutional neural networks (CNNs) have long dominated have long dominated image processing, the Vision Transformer is emerging to offer an innovative approach to artificial intelligence. It's worth remembering that expert data labeling is important for maximizing the accuracy and efficiency 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 offer 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 the operations of CNN networks with self-attention mechanisms. In short, we explain it all!
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 image parts.
By dividing the image into patches and applying self-attention operations, it captures spatial and semantic interactions. This provides a global representation of the image. With layers of self-attention and feed-forward transformation, it learns hierarchical visual features.
This approach opens up new perspectives in object recognitionrecognition image segmentationin the field of computer vision. The results obtained using 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 sequences of data. Each patch is represented by a vector, and then each pair of vectors is evaluated for their relationships thanks to auto-attention mechanisms.
These mechanisms enable the model to capture spatial and semantic interactions between patches, focusing on the relevant parts of the image. This information is then propagated through several layers of feed-forward transformation, enabling the model to learn hierarchical and abstract representations of the image.
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Where does the Vision Transformer come from?
The Vision Transformer (or ViT) was originally developed for natural language processing, 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. So it's (relatively) recent!
The fundamental idea behind ViT is to process images as sequences of "patches" (or chunks) rather than individual pixels. These patches are then processed by a Transformer model, which is able to capture the long-range dependencies between the different elements of the sequence.
What influence does ViT have on AI?
The innovative architecture of the Vision Transformer (ViT) merges the concepts of convolutional neural networks and Transformer models. Its many influences include
Transformers in the NLP
The main influence came from the Transformers models, which revolutionized natural language processing. Attention mechanisms have been particularly effective in improving the comprehension of English sentences and their translation into French. Models such as BERT, GPT and others have demonstrated the effectiveness of attention mechanisms in capturing sequential relations.
Convolutional neural networks (CNN)
Although ViT uses a Transformer architecture, its initial application domain is heavily influenced by CNNs, which have long dominated AI developments in this field (and are still used successfully, incidentally). The latter are excellent for capturing local patterns patterns 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 enables the model to weight different parts of the input data according to their importance for a given task. For example, this mechanism can be used to determine the importance of each word relative to the others in the context of a sentence. This idea has been successfully extended to the processing of image data in ViT.
Fundamental to Transformers, and therefore to ViT, is the concept of self-attention, where each element of a sequence (or image, in ViT's case) can interact with all other elements. This enables the model to capture contextual dependencies, improving model "understanding" 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, which are mainly based on convolutional neural networks (CNNs), ViT applies the mechanisms of Transformers. This approach enables ViT to capture long-range relationships between different image elements more efficiently.
Image patch processing
Rather than processing each pixel individually, ViT divides the image into patches (or chunks) and treats them as a sequence of data. This allows the model to handle images of varying sizes without the need for image-size-specific convolutions.
Global self-attention
Unlike CNNs, which use convolution operations to extract local features, ViT uses global self-attention mechanisms that allow each element in the image to interact with all the others. This enables the model to capture long-range relationships and complex patterns in the image.
Scalability
ViT is highly scalable, meaning it can be trained on large amounts of data and adapted to different image sizes without requiring major modifications to its architecture. This makes it a versatile and adaptable architecture for a variety of computer vision tasks.
What are the Vision Transformer's typical applications?
The Vision Transformer (ViT) has proved its worth in a variety of computer vision applications.
Image classification
ViT can be used for image classificationwhere it is trained to recognize and classify different objects, scenes or image categories. It has demonstrated comparable or even superior performance to traditional CNN architectures in this task.
Object detection
Although CNNs have traditionally dominated object object detectionViT is also capable of handling this task successfully. Using techniques such as multi-scale object detection and the integration of self-attention mechanisms, ViT can efficiently detect and locate objects in an image.
Semantic segmentation
ViT can be used for semantic segmentationwhere the aim is to assign a semantic label to each image pixel. By exploiting ViT's self-attention capabilities, it is possible to capture the spatial relationships between different image elements and perform precise segmentation.
Stock recognition
ViT can be used for action recognition in videosViT can be used for action recognition in videos, where the aim 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 of 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, good-quality images from a textual 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 fields.
In conclusion
The Vision Transformer (ViT) marks a significant advance in computer vision, exploiting self-attention mechanisms to process images in a more global and contextual way. Inspired by the success of transformers in natural language processing, the ViT replaces convolutional operations with self-attention techniques, enabling the capture of richer, more complex spatial and semantic relationships within images.
With applications ranging from image classification and semantic segmentation to object detection and action recognition, the Vision Transformer is proving its efficiency and versatility. Its innovative, scalable approach offers promising prospects for many 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. Many startups, for example, are exploring partnerships with data annotation companies (such as InnovatianaBy enabling more precise and contextualized image analysis, these services pave the way for even more advanced innovations in the future, using innovative methods such as Vision Transformers.
