Advances in Deep Reinforcement Learning for Computer Vision Applications

Zhengyang Li

doi:10.70393/6a69656173.323234

Authors

Zhengyang Li DigiPen Institute of Technology

DOI:

https://doi.org/10.70393/6a69656173.323234

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n6a03

Disciplines:

Computer Science

Subjects:

Computer Vision

References:

26

Keywords:

Deep Reinforcement Learning, Computer Vision, Object Detection，Q-learning

Abstract

Deep Reinforcement Learning (DRL) has become very popular for computer vision (CV), solving mostly visually complex environments with decision making and dynamic adaption to different situations. This article provides an introduction to the basic concepts of deep reinforcement learning with a special focus on their applications in computer vision tasks, including challenging problems and emerging solutions. It reviews various DRL algorithms such as Q-learning, policy gradient methods, and Actor-Critic models, explaining much of the modifications that are done to make it work on high-dimensional visual problems. Different applications of DRL in major CV applications like object detection, image segmentation, target tracking and image generation are reviewed to demonstrate the power as well as limitations of DRL in practice. More importantly, the novel paradigms such as hierarchical policy learning, adaptive reward design multi-task reinforcement learning and domain adaptation can be viewed as promising premises to improve model efficiency and generalizability in multiple scenarios. Finally, this paper mentions a few of the existing challenges like computational power cost and sample efficiency; as well as future paths for enhancing that can widen devout reinforcement learning in computer vision. Through this comprehensive overview, we aim to shed light on the promising synergies between DRL and CV, while identifying key areas for future research and application.

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Author Biography

Zhengyang Li, DigiPen Institute of Technology

DigiPen Institute of Technology, Redmond, WA, USA.

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