A Survey on Deep Learning Technique for Video Segmentation

Overview

A Survey on Deep Learning Technique for Video Segmentation

A Survey on Deep Learning Technique for Video Segmentation
Wenguan Wang, Tianfei Zhou, Fatih Porikli, David Crandall, and Luc Van Gool.
paper

Contributing

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Welcome any discussions on video segmentation at Gitter

1. Introduction

Video segmentation, i.e., partitioning video frames into multiple segments or objects, plays a critical role in a broad range of practical applications, from enhancing visual effects in movie, to understanding scenes in autonomous driving, to virtual background creation in video conferencing. In this survey, we comprehensively review two basic lines of research — video object segmentation and video semantic segmentation — by introducing their respective task settings, background concepts, perceived need, development history, and main challenges. In particular, we review eight sub-fields as given in the following figure:

2. Deep Learning-based Video Object Segmentation

3. Deep Learning-based Video Semantic Segmentation

4. Datasets

Citation

If you find our survey and repository useful for your research, please consider citing our paper:

@article{wang2021survey,
  title={A survey on deep learning technique for video segmentation},
  author={Wang, Wenguan and Zhou, Tianfei and Porikli, Fatih and Crandall, David and Van Gool, Luc},
  journal={arXiv preprint arXiv:2107.01153},
  year={2021}
}
Owner
Tianfei Zhou
Tianfei Zhou
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