Transformers and Visual Transformers

Courant, Robin; Maika, Edberg,; Dufour, Nicolas; Kalogeiton, Vicky

doi:10.1007/978-1-0716-3195-9_6

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Since its introduction, it has revolutionized Deep Learning, making breakthroughs in a variety of fields such as Natural Language Processing, Computer Vision, Chemistry, and Biology on its way to becoming the default architecture for learning representations. The standard Transformer [4] has recently been adapted for vision tasks [5]. Once again, visual Transformer has emerged as a key architecture in computer vision.…”

Section: Visual Transformersmentioning

confidence: 99%

Analysis of Transformer-Deep Neural Network Using Deep Learning

Kumari,

Kaur,

Rawat

et al. 2023

IJEAST

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Transformers were first used for natural language processing (NLP) tasks, but they quickly spread to other deep learning fields, including computer vision. They assess the interdependence of pairs. Attention is a part that enables to dynamically highlight relevant features of the input data (words in the case of text strings, parts of images in the case of visual Transformers). The cost grows continually with the number of tokens. The most common Trans- former Architecture for image classification uses only the Transformer Encoder to transform the various input tokens. However, the decoder component of the traditional Transformer Architecture is also used in a variety of other applications. In this section, we first introduce the Attention Mechanism (Section 1), followed by the Basic Transformer Block, which includes the Vision Transformer (Section 2).

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Section: Visual Transformersmentioning

confidence: 99%

Analysis of Transformer-Deep Neural Network Using Deep Learning

Kumari,

Kaur,

Rawat

et al. 2023

IJEAST

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Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

Hcini,

Jdey,

Dhahri

2024

Neural Process Lett

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Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques.

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MD-TransUNet: An Image Segmentation Network for Car Front Face Design

Ouyang,

Shi,

et al. 2024

Applied Sciences

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To enhance the segmentation accuracy of car front face elements such as headlights and grilles for car front face design, and to improve the superiority and efficiency of solutions in automotive partial modification design, this paper introduces MD-TransUNet, a semantic segmentation network based on the TransUNet model. MD-TransUNet integrates multi-scale attention gates and dynamic-channel graph convolution networks to enhance image restoration across various design drawings. To improve accuracy and detail retention in segmenting automotive front face elements, dynamic-channel graph convolution networks model global channel relationships between contextual sequences, thereby enhancing the Transformer’s channel encoding capabilities. Additionally, a multi-scale attention-based decoder structure is employed to restore feature map dimensions, mitigating the loss of detail in the local feature encoding by the Transformer. Experimental results demonstrate that the MSAG module significantly enhances the model’s ability to capture details, while the DCGCN module improves the segmentation accuracy of the shapes and edges of headlights and grilles. The MD-TransUNet model outperforms existing models on the automotive front face dataset, achieving mF-score, mIoU, and OA metrics of 95.81%, 92.08%, and 98.86%, respectively. Consequently, the MD-TransUNet model increases the precision of automotive front face element segmentation and achieves a more advanced and efficient approach to partial modification design.

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Transformers and Visual Transformers

Cited by 3 publications

References 32 publications

Analysis of Transformer-Deep Neural Network Using Deep Learning

Analysis of Transformer-Deep Neural Network Using Deep Learning

Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

MD-TransUNet: An Image Segmentation Network for Car Front Face Design

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