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软件学报 ISSN 1000-9825, CODEN RUXUEW                                        E-mail: jos@iscas.ac.cn
                 2026,37(4):1759−1776 [doi: 10.13328/j.cnki.jos.007460] [CSTR: 32375.14.jos.007460]  http://www.jos.org.cn
                 ©中国科学院软件研究所版权所有.                                                          Tel: +86-10-62562563



                                                     *
                 基于分类检索的操作规划方法

                 吴益露,    王瀚霖,    王利民


                 (南京大学 计算机学院, 江苏 南京 210023)
                 通信作者: 王利民, E-mail: lmwang@nju.edu.cn

                 摘 要: 聚焦于教学视频       (instructional video) 中的操作规划  (procedure planning) 问题, 探讨如何根据给定的开始和
                 结束视觉状态, 在教学视频提供的动作空间中规划出一条将开始状态转变为结束状态的动作序列. 教学视频以记
                 录和展示各种事件的操作过程为特点, 每个事件对应一组特定动作, 从而形成事件的动作空间. 多个事件的动作空
                 间共同构成了教学视频的整体动作空间. 传统方法未能充分挖掘事件的语义信息, 过于依赖强化学习等复杂训练
                 方法, 既增加了算法设计的复杂性, 又导致模型的可解释性较差. 针对这些问题, 结合教学视频的特点, 提出了一种
                 基于分类检索的操作规划器          CPP (classification-retrieval-based procedure planner), 分阶段解决操作规划任务. 具体
                 而言, 该方法首先通过视觉状态识别事件类别, 将动作空间限定在一个较小的子空间内, 显著降低规划的复杂性;
                 随后, 在该子空间中进行动作序列的规划. 此外, 提出了一种混合规划策略, 将动作序列的检索与预测相结合, 进一
                 步提升了规划性能. 实验结果表明, 该方法在             3  个不同规模的教学视频数据集上均取得了显著效果, 为操作规划任
                 务提供了一种简单而高效的基准方法.
                 关键词: 操作规划; 教学视频; 视觉推理
                 中图法分类号: TP18

                 中文引用格式: 吴益露, 王瀚霖, 王利民. 基于分类检索的操作规划方法. 软件学报, 2026, 37(4): 1759–1776. http://www.jos.org.cn/
                 1000-9825/7460.htm
                 英文引用格式: Wu YL, Wang HL, Wang LM. Classification-retrieval-based Procedure Planning Method. Ruan Jian Xue Bao/Journal
                 of Software, 2026, 37(4): 1759–1776 (in Chinese). http://www.jos.org.cn/1000-9825/7460.htm

                 Classification-retrieval-based Procedure Planning Method

                 WU Yi-Lu, WANG Han-Lin, WANG Li-Min
                 (School of Computer Science, Nanjing University, Nanjing 210023, China)
                 Abstract:  This  study  focuses  on  the  problem  of  procedure  planning  in  instructional  videos.  Given  the  start  and  end  observations,  the  task
                 is  to  plan  an  action  sequence  that  transforms  the  start  state  into  the  end  state  within  the  action  space  provided  by  the  instructional  videos.
                 Instructional  videos  record  and  demonstrate  the  operational  processes  of  various  events.  Each  event  includes  a  specific  set  of  actions,
                 forming  the  action  space  for  that  event.  Therefore,  the  action  space  is  composed  of  various  subspaces  corresponding  to  different  events  in
                 instructional  videos.  Previous  methods  fail  to  effectively  utilize  the  semantic  information  of  events  and  overly  rely  on  techniques  such  as
                 reinforcement  learning,  resulting  in  complex  training  schemes  and  poorly  explainable  approaches.  In  contrast,  this  study  considers  the
                 characteristics  of  instructional  videos  and  proposes  the  classification-retrieval-based  procedure  planner  (CPP),  a  pipeline  that  addresses
                 procedure  planning  from  coarse  to  fine.  Specifically,  the  planner  first  identifies  the  event  category  based  on  the  given  observations,
                 narrowing  the  action  space  to  a  smaller  subspace.  Then,  action  planning  is  performed  within  the  selected  subspace,  which  is  significantly
                 easier  than  planning  in  the  entire  action  space.  Moreover,  this  study  introduces  a  hybrid  planning  method  that  combines  retrieval  and
                 prediction  approaches  to  generate  the  action  sequence.  The  proposed  method  achieves  competitive  results  on  three  popular  procedure
                 planning datasets of varying scales, establishing itself as a simple yet robust baseline for procedure planning.
                 Key words:  procedure planning; instructional video; visual reasoning


                 *    基金项目: 科技创新 2030—“新一代人工智能”重大项目   (2022ZD0160900)
                  收稿时间: 2025-01-07; 修改时间: 2025-02-10; 采用时间: 2025-04-29; jos 在线出版时间: 2025-11-05
                  CNKI 网络首发时间: 2025-11-06
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