A Survey on Deep Learning Technique for Video Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Tianfei Zhou
,
Fatih Porikli
,
David Crandall
,
Luc Van Gool
,
Wenguan Wang
This repo compiles a collection of resources on deep video segmentation, and will be continuously updated to track developments in the field. Please feel free to submit a pull request if you find any work missing.
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 β video object segmentation and video semantic segmentation β 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
- 2.1 Automatic Video Object Segmentation (AVOS)
- 2.2 Semi-automatic Video Object Segmentation (SVOS)
- 2.3 Interactive Video Object Segmentation (IVOS)
- 2.4 Language-guided Video Object Segmentation (LVOS)
3. Deep Learning-based Video Semantic Segmentation
- 3.1 (Instance-agnostic) Video Semantic Segmentation (VSS)
- 3.2 Video Instance Segmentation (VIS)
- 3.3 Video Panoptic Segmentation (VPS)
4. Datasets
Citation
If you find our survey and repository useful for your research, please consider citing our paper:
@article{zhou2023survey,
title={A Survey on Deep Learning Technique for Video Segmentation},
author={Zhou, Tianfei and Porikli, Fatih and Crandall, David J and Van Gool, Luc and Wang, Wenguan},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2023},
publisher={IEEE}
}