Read on arXivLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

One Sentence Abstract

The Vision Transformer (ViT) outperforms convolutional networks on image classification tasks, achieving excellent results with fewer computational resources, by directly applying Transformer architecture to sequences of image patches.

Simplified Abstract

Researchers are working on improving image recognition, which is a computer's ability to identify objects in pictures. Traditionally, this has been done using a method called convolutional networks (CNNs). However, a new method called the Vision Transformer (ViT) is showing promising results. Instead of relying on CNNs, ViT directly analyzes a sequence of small picture pieces called "image patches."

The researchers found that ViT performs very well in image classification tasks, even better than current CNN methods, while using fewer computational resources. This means that ViT can recognize images with higher accuracy and with less need for powerful computers. The researchers hope that their new approach will help improve image recognition in various applications, from self-driving cars to medical imaging.

To summarize, this study introduces a new technique for image recognition that is more efficient and accurate than the traditional method. By focusing on the image patches and not relying on CNNs, the researchers have created a powerful tool that could transform the way we use computers to identify objects in images.

Study Fields

Main fields:

• Natural Language Processing (NLP)
• Computer Vision

Subfields:

• Transformer architecture
• Attention in Computer Vision
• Convolutional Neural Networks (CNNs)
• Image Classification
• Pre-training and Transfer Learning
• Benchmarks (ImageNet, CIFAR-100, VTAB)
• Computational Resources

Study Objectives

• Investigate the applicability of Transformer architecture to computer vision tasks
• Determine if a pure transformer applied directly to sequences of image patches can perform well on image classification tasks
• Compare the performance of Vision Transformer (ViT) with state-of-the-art convolutional networks in terms of results and computational resources required for training
• Evaluate the performance of ViT on multiple image recognition benchmarks, such as ImageNet, CIFAR-100, and VTAB
• Make the fine-tuning code and pre-trained models of ViT available for public use through GitHub (https://github.com/google-research/vision_transformerLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.)

Conclusions

• The Transformer architecture, commonly used in natural language processing, can also be applied directly to sequences of image patches in computer vision tasks.
• The reliance on convolutional networks (CNNs) in computer vision is not necessary, as a pure transformer can perform well on image classification tasks.
• Vision Transformer (ViT) achieves excellent results when pre-trained on large amounts of data and transferred to multiple image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), surpassing or matching state-of-the-art convolutional networks.
• ViT requires substantially fewer computational resources to train compared to conventional methods, making it a more efficient approach.
• The code and pre-trained models for Vision Transformer are available at https://github.com/google-research/vision_transformerLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat..

References