Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey

TL;DR

This survey comprehensively reviews recent advances in deep reinforcement learning applied to various computer vision tasks, analyzing methodologies, datasets, and future research directions.

Contribution

It categorizes deep reinforcement learning applications in computer vision into seven main areas and discusses their techniques, advantages, limitations, and future challenges.

Findings

Deep reinforcement learning has been successfully applied across multiple computer vision tasks.

The survey identifies key datasets and source code resources for further research.

Open issues and future directions are discussed to guide upcoming research efforts.

Abstract

Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i)landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision