Deep Learning For Computer Vision Tasks: A review

TL;DR

This review paper summarizes key deep learning algorithms used in computer vision, discussing their applications in image classification, object detection, and segmentation, along with future challenges and research directions.

Contribution

It provides a comprehensive overview of widely used deep learning methods in computer vision and discusses future challenges in their development and training.

Findings

Deep learning algorithms significantly improve image classification accuracy.

Object detection and segmentation benefit from advanced deep neural network architectures.

Future challenges include training efficiency and robustness of deep models.

Abstract

Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems. This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision. It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction and semantic segmentation in the presence of noise. The paper concludes with the discussion of the future scope and challenges for construction and training of deep neural networks.