Design and Implementation of Transformer Model with Visual Learning Approach
Mrs.Pranali Warhade
Assistant Professor Artificial Intelligence & Data Science Priyadarshini College of Engineering Nagpur, Maharashtra
Neeraj Vaidhya
Artificial Intelligence & Data Science Priyadarshini College of Engineering Nagpur, Maharashtra
Kunal Pise
Artificial Intelligence & Data Science Priyadarshini College of Engineering Nagpur, Maharashtra
Sonit Shahare
Artificial Intelligence & Data Science Priyadarshini College of Engineering
Nagpur, Maharashtra
ABSTRACT
Deep learning techniques have brought a major transformation in the field of computer vision, with Convolutional Neural Networks (CNNs) playing a key role in achieving high performance across tasks such as image classification, object detection, and segmentation. CNNs are highly effective in extracting local features like edges, textures, and patterns. However, they often struggle to capture long-range dependencies and global contextual relationships within an image, which can limit their performance in more complex visual understanding tasks. To address these limitations, Vision Transformers (ViTs) have recently emerged as a powerful alternative. Inspired by transformer architectures originally developed for Natural Language Processing (NLP), ViTs utilize self-attention mechanisms to model relationships between different regions of an image, enabling a more comprehensive understanding of global features. This ability allows them to capture both local and long-distance interactions more effectively than traditional CNN-based approaches.
This review paper provides a detailed overview of Vision Transformer architecture, including its core components, working principles, and advantages over conventional CNN models. It also explores various applications of ViTs in visual learning tasks such as image classification, medical imaging, and object detection. Additionally, the paper discusses key challenges associated with Vision Transformers, including their high computational cost, dependency on large-scale datasets, and training complexity. Finally, potential solutions and future research directions are highlighted to improve the efficiency, scalability, and practical applicability of Vision Transformer models in real-world scenarios.
