The Role of Deep Learning in Computer Vision

EasyChair Preprint 12424

Abstract

This paper explores the pivotal role of deep learning in the field of computer vision. Computer vision, the study of enabling machines to perceive and understand visual information, has witnessed significant advancements with the advent of deep learning techniques. Traditional computer vision approaches faced limitations in handling complex visual tasks, motivating the need for advanced methods. Deep learning, powered by neural networks and convolutional neural networks (CNNs), has revolutionized computer vision by offering end-to-end learning, feature representation, and adaptability. The paper discusses various applications of deep learning in computer vision, including image classification, object detection, semantic segmentation, and video analysis. It also addresses the advantages of deep learning, such as its ability to handle large-scale datasets and generalize well. However, challenges and limitations, including the need for labeled data and computational requirements, are examined. The paper concludes by highlighting recent advances and future directions, such as transfer learning, generative adversarial networks (GANs), and attention mechanisms, underscoring the importance of ongoing research and development in this rapidly evolving field. Overall, deep learning has emerged as a pivotal tool in computer vision, with the potential to significantly impact various domains and applications.

Keyphrases: computer vision, deep learning, machine learning

@booklet{EasyChair:12424,
  author    = {Abil Robert},
  title     = {The Role of Deep Learning in Computer Vision},
  howpublished = {EasyChair Preprint 12424},
  year      = {EasyChair, 2024}}