发表机构
Singapore University of Technology and Design; Institute for Infocomm Research, A*STAR（新加坡科技设计大学; 资讯通信研究院，新加坡科技研究局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对长增量3D目标检测中模型易遗忘问题，提出LDMR框架，通过监测训练检查点的类检测质量，驱动类人阶段内回顾和场景感知跨阶段记忆进化机制，有效减轻模型遗忘，性能优于基线。
AI 中文摘要
增量3D目标检测要求检测器在顺序接收的数据上学习新的目标类别，同时记住先前学习的类别。以前基于伪标签的方法在短增量阶段表现尚可，但处理长增量序列时仍会严重遗忘。研究发现有害的自我强化循环：新类数据分布变化导致旧类遗忘，进而在伪标签中产生累积错误加剧模型退化。为此提出LDMR框架，在定期训练检查点监测类检测质量，驱动类人阶段内回顾和场景感知跨阶段记忆进化机制。在多个室内基准上的实验表明，LDMR显著减轻模型遗忘，明显优于所有基线。
英文摘要
Incremental 3D object detection requires a detector to learn novel object classes while remembering previously learned ones over sequentially arriving data. Previous methods, primarily based on pseudo-labeling, perform reasonably in short-incremental stages but still suffer from severe model forgetting when dealing with long-incremental sequences. We investigate this failure and reveal a detrimental self-reinforcing cycle: data distribution shift of novel classes causes model forgetting on old classes, which further produces accumulated error in pseudo-labeling that exacerbates model degradation. To address this issue, we draw inspiration from the human learning process and propose the \emph{Learning-Dynamics-driven Memory and Review} (LDMR) framework. LDMR monitors per-class detection quality at periodic training checkpoints and uses these learning-dynamics signals to drive two innovative mechanisms, namely (i) human-like intra-stage review that divides each incremental stage into multiple sub-stages' training and concentrates on remembering the most-forgotten objects, and (ii) scene-aware cross-stage memory evolution that evolves a memory bank to transfer knowledge between two consecutive stages by jointly considering scene learnability and diversity. Extensive experiments across multiple long-incremental protocols on indoor benchmarks SUN RGB-D and ScanNetV2 show that LDMR substantially mitigates the model forgetting and outperforms all baselines by a clear margin. Code is available at https://github.com/qianpeisheng/LDMR.
CommentsECCV 2026
