A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships
This is an incremental review summarizing existing transformer architectures for computer vision, aimed at researchers and practitioners in the field.
This review paper provides an extensive overview of transformer-based models adapted for computer vision tasks, highlighting their ability to capture global context and spatial relationships to excel in tasks like image classification, object detection, and segmentation, and discusses their strengths, limitations, and future research directions.
Transformer-based models have transformed the landscape of natural language processing (NLP) and are increasingly applied to computer vision tasks with remarkable success. These models, renowned for their ability to capture long-range dependencies and contextual information, offer a promising alternative to traditional convolutional neural networks (CNNs) in computer vision. In this review paper, we provide an extensive overview of various transformer architectures adapted for computer vision tasks. We delve into how these models capture global context and spatial relationships in images, empowering them to excel in tasks such as image classification, object detection, and segmentation. Analyzing the key components, training methodologies, and performance metrics of transformer-based models, we highlight their strengths, limitations, and recent advancements. Additionally, we discuss potential research directions and applications of transformer-based models in computer vision, offering insights into their implications for future advancements in the field.