Continual Learning Methods and Applications in Computer Vision

FANG Yan; Wei Yun-chao; Cong Run-min; Zuo Wang-meng; Zhao Yao

doi:10.12263/DZXB.20240750

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Continual Learning Methods and Applications in Computer Vision

Large-Scale Models and the Internet | 更新时间：2025-10-16

- Continual Learning Methods and Applications in Computer Vision
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 1713-1740(2025)
- 作者机构：
  
  1.北京交通大学计算机科学与技术学院，北京 100044
  2.山东大学控制科学与工程学院，山东济南 250100
  3.哈尔滨工业大学计算机学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China: New Generation Artificial Intelligence Major Project(2021ZD0112100)
- DOI：10.12263/DZXB.20240750
  CLC： TP18;
- Received：13 August 2024，
  
  Revised：2025-05-26，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
方岩, 魏云超, 丛润民, 等. 连续学习方法与其在视觉任务中的应用[J]. 电子学报, 2025, 53(06): 1713-1740.

FANG Yan, Wei Yun-chao, Cong Run-min, et al. Continual Learning Methods and Applications in Computer Vision[J]. Acta Electronica Sinica, 2025, 53(06): 1713-1740.
方岩, 魏云超, 丛润民, 等. 连续学习方法与其在视觉任务中的应用[J]. 电子学报, 2025, 53(06): 1713-1740. DOI：10.12263/DZXB.20240750

FANG Yan, Wei Yun-chao, Cong Run-min, et al. Continual Learning Methods and Applications in Computer Vision[J]. Acta Electronica Sinica, 2025, 53(06): 1713-1740. DOI：10.12263/DZXB.20240750

摘要

连续学习广义上指智能算法与智能体学习和适应动态变化世界的能力，这使得智能算法能够在部署周期中不断获取、更新、积累和利用知识.连续学习技术赋予了智能系统自适应发展的前景和能力.在深度学习中，连续学习具体指的是能够从非平稳数据流中学习和适应不断变化的训练目标，这一任务通常面临着灾难性遗忘的挑战，即学习新任务通常会导致旧任务性能的大幅下降.近年来，随着深度学习在语言、视觉等诸多领域的迅速发展，涌现了诸多进展，有效拓展了对连续学习的理解和应用.本工作对现有连续学习工作进行了较为广泛而深入的调研，并从连续学习基础定义、代表性方法、在视觉领域的应用等多角度分析.最后，本文也对连续学习当前的前沿发展和未来研究趋势进行探讨.基于对连续学习领域相关工作的探讨，期待本文这一综述可以有效促进该领域和后续研究工作的进一步发展和探索.

Abstract

Continual learning generally refers to the ability of intelligent algorithms and agents to learn and adapt to a dynamic and changing world

enabling these algorithms to continually acquire

update

accumulate

and utilize knowledge throughout their deployment cycle. Continual learning technologies endow intelligent systems with the prospects and capabilities of adaptive development. In the context of deep learning

continual learning specifically refers to the ability to learn from non-stationary data streams and adapt to changing training objectives. This task often faces the challenge of catastrophic forgetting

where learning new tasks typically results in a significant decline in performance on previously learned tasks. In recent years

with the rapid development of deep learning in various fields such as language and vision

numerous advancements have emerged

effectively extending the understanding and application of continual learning. This work conducts a relatively extensive and comprehensive survey of existing continual learning research

analyzing it from multiple perspectives including fundamental definition

representative methods

applications in the visual domain. Finally

this paper also discusses the current cutting-edge developments and future research trends in continual learning. Based on the discussion of relevant work in the field of continual learning

we hope this review can effectively promote the development and exploration of this field and subsequent research endeavors.

关键词

Keywords

references

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