基于无标签视频数据的深度预测学习方法综述

潘敏婷; 王韫博; 朱祥明; 高思宇; 龙明盛; 杨小康

doi:10.12263/DZXB.20211209

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基于无标签视频数据的深度预测学习方法综述

机器学习交叉融合创新 | 更新时间：2025-12-08

- 基于无标签视频数据的深度预测学习方法综述
- A Survey on Deep Predictive Learning Based on Unlabeled Videos
- 电子学报 2022年50卷第4期页码：869-886
- 作者机构：
  
  1.上海交通大学人工智能研究院、人工智能教育部重点实验室，上海 201109
  2.清华大学软件学院，北京 100084
- 作者简介：
  
  [ "潘敏婷女，1996年生，广西贵港人.现为上海交通大学博士研究生.主要研究方向为视频数据预测模型.E-mail: panmt53@sjtu.edu.cn" ]
  [ "王韫博（通讯作者）男，1989年生，吉林长春人.现为上海交通大学人工智能研究院助理教授.主要研究方向为深度学习，尤其是预测学习、时空动态系统建模、有模型的强化学习决策.E-mail: yunbow@sjtu.edu.cn" ]
  高思宇女，1999年生，山东青岛人.现为上海交通大学硕士研究生.主要研究方向为时空序列数据预测.E-mail: siyu.gao@sjtu.edu.cn
  龙明盛男，1985年生，广西河池人.现为清华大学副教授，国家优秀青年科学基金获得者.主要研究方向为机器学习的理论和算法，尤其是迁移学习、深度学习和面向科学的机器学习方法.E-mail: mingsheng@tsinghua.edu.cn
  杨小康男，1972年生，浙江东阳人.现为上海交通大学教授，教育部长江学者特聘教授，国家杰出青年科学基金获得者，国家万人计划创新领军人才.主要研究方向为计算机视觉和机器学习.E-mail: xkyang@sjtu.edu.cn
- 基金信息：
  
  国家自然科学基金(62106144;U19B2035);上海市科技重大专项(2021SHZDZX0102);上海市青年科技英才扬帆计划(21Z510202133)
- DOI：10.12263/DZXB.20211209
  中图分类号： TP389.1;
- 收稿：2021-09-01，
  
  修回：2022-02-17，
  
  纸质出版：2022-04-25
- 稿件说明：
移动端阅览
潘敏婷,王韫博,朱祥明等.基于无标签视频数据的深度预测学习方法综述[J].电子学报,2022,50(04):869-886.

PAN Min-ting,WANG Yun-bo,ZHU Xiang-ming,et al.A Survey on Deep Predictive Learning Based on Unlabeled Videos[J].ACTA ELECTRONICA SINICA,2022,50(04):869-886.
潘敏婷,王韫博,朱祥明等.基于无标签视频数据的深度预测学习方法综述[J].电子学报,2022,50(04):869-886. DOI： 10.12263/DZXB.20211209.

PAN Min-ting,WANG Yun-bo,ZHU Xiang-ming,et al.A Survey on Deep Predictive Learning Based on Unlabeled Videos[J].ACTA ELECTRONICA SINICA,2022,50(04):869-886. DOI： 10.12263/DZXB.20211209.

摘要

基于视频数据的深度预测学习（以下简称“深度预测学习”）属于深度学习、计算机视觉和强化学习的交叉融合研究方向，是气象预报、自动驾驶、机器人视觉控制等场景下智能预测与决策系统的关键组成部分，在近年来成为机器学习的热点研究领域.深度预测学习遵从自监督学习范式，从无标签的视频数据中挖掘自身的监督信息，学习其潜在的时空模式表达.本文对基于深度学习的视频预测现有研究成果进行了详细综述.首先，归纳了深度预测学习的研究范畴和交叉应用领域.其次，总结了视频预测研究中常用的数据集和评价指标.而后，从基于观测空间的视频预测、基于状态空间的视频预测、有模型的视觉决策三个角度，分类对比了当前主流的深度预测学习模型.最后，本文分析了深度预测学习领域的热点问题，并对研究趋势进行了展望.

Abstract

Deep predictive learning based on video data (hereinafter referred to as "deep predictive learning") is a research direction of deep learning

being interacted with computer vision and reinforcement learning. It is a key part of intelligent prediction and decision-making systems in weather forecasting

autonomous driving

robotics

and other scenarios

and has become a hot research field of machine learning in recent years. Deep predictive learning follows the self-supervised learning paradigm

using internal constraints from unlabeled video data to learn the underlying spatiotemporal patterns. In this paper

we review the existing deep learning techniques for predictive learning in detail. First

we summarize the research scope and application fields of deep predictive learning. Second

we present the datasets and evaluation metrics commonly used in this research field. Third

we summarize current mainstream deep prediction learning models from three perspectives: predictive models based on observation space

predictive models based on state space

and visual planning methods based on the predictive models. Finally

we discuss the hot issues and future research directions in the field of deep predictive learning.

关键词

Keywords

references

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