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A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships

Gracile Astlin Pereira, Muhammad Hussain

TL;DR

Transformer-based vision models enable holistic image understanding by capturing global context and spatial relationships via self-attention. This paper surveys architectures such as ViT, DETR, SMCA, SWIN, Anchor DETR, and Deformable DETR, detailing internal components, training strategies, and benchmark performance on ImageNet and COCO. It discusses strengths, limitations, and future directions, including efficiency, inductive biases, and scalability. The synthesis helps researchers and practitioners select suitable models for real-world object detection, segmentation, and classification tasks.

Abstract

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.

A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships

TL;DR

Transformer-based vision models enable holistic image understanding by capturing global context and spatial relationships via self-attention. This paper surveys architectures such as ViT, DETR, SMCA, SWIN, Anchor DETR, and Deformable DETR, detailing internal components, training strategies, and benchmark performance on ImageNet and COCO. It discusses strengths, limitations, and future directions, including efficiency, inductive biases, and scalability. The synthesis helps researchers and practitioners select suitable models for real-world object detection, segmentation, and classification tasks.

Abstract

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.
Paper Structure (19 sections, 6 figures, 2 tables)

This paper contains 19 sections, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Transformer Architectures for Computer Vision
  3. Vision Transformer (ViT)
  4. Internal Architecture Breakdown:
  5. DEtection TRansformer (DETR):
  6. Internal Architecture Breakdown:
  7. Spatially Modulated Co-Attention (SMCA):
  8. Internal Architecture Breakdown:
  9. SWIN Transformer:
  10. Internal Architecture Breakdown:
  11. Anchor DETR:
  12. Internal Architecture Breakdown:
  13. DEformable TRansformer (DETR):
  14. Internal Architecture Breakdown:
  15. Capturing Global Context
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Vision Transformer Architecture
  • Figure 2: DEtection TRansformer Architecture
  • Figure 3: Spatially Modulated Co-Attention Architecture
  • Figure 4: SWIN Transformer Architecture
  • Figure 5: Anchor DEtection TRansformer Architecture
  • ...and 1 more figures