解锁手术器械增量学习中的正迁移：自反思层级提示词框架
Unlocking Positive Transfer in Incrementally Learning Surgical Instruments: A Self-reflection Hierarchical Prompt Framework
在冻结的预训练骨干上，用层级提示词树组织类别知识，并通过有向加权图传播做自反思式知识精炼，为视觉中的类增量分割提供了一种可复用的正迁移机制，可直接迁移到其他增量分割或增量检测设定。
原文依据 we propose a self-reflection hierarchical prompt framework that unlocks the power of positive forward and backward knowledge transfer in class incremental segmentation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论为持续提升模型在外科视频场景解析中的适应性，近期研究通过增量更新模型，使其随时间推移逐步学习分割数量不断增加的手术器械。然而，已有工作始终忽视了正向知识迁移的潜力，即过去的知识如何帮助学习新类别，以及反向知识迁移的潜力，即学习新类别如何帮助精炼过去的知识。本文提出一种自反思层级提示词框架，在类增量分割中释放正向与反向知识迁移的能力，旨在熟练学习新器械、提升常规器械的既有技能，并避免对旧器械的灾难性遗忘。该框架构建于一个冻结的预训练模型之上，在训练回合中为新类别自适应地追加器械感知提示词。为实现正向知识迁移，我们将器械提示词组织为一棵层级提示词解析树，以器械共享提示词分区为根节点、n 个部件共享提示词分区为中间节点、器械特有提示词分区为叶节点，从而为新类别暴露可复用的历史知识以简化其学习。反之，为促进反向知识迁移，我们通过有向加权图传播对既有知识进行自反思精炼，考察记录在树中的知识关联，以提升其代表性而不引发灾难性遗忘。该框架适用于基于 CNN 的模型与先进的基于 transformer 的基础模型，在两个公开基准上分别相较对比方法取得超过 5% 与 11% 的提升。
英文原文摘要 1829 字符
To continuously enhance model adaptability in surgical video scene parsing, recent studies incrementally update it to progressively learn to segment an increasing number of surgical instruments over time. However, prior works constantly overlooked the potential of positive forward knowledge transfer, i.e., how past knowledge could help learn new classes, and positive backward knowledge transfer, i.e., how learning new classes could help refine past knowledge. In this paper, we propose a self-reflection hierarchical prompt framework that unlocks the power of positive forward and backward knowledge transfer in class incremental segmentation, aiming to proficiently learn new instruments, improve existing skills of regular instruments, and avoid catastrophic forgetting of old instruments. Our framework is built on a frozen, pre-trained model that adaptively appends instrument-aware prompts for new classes throughout training episodes. To enable positive forward knowledge transfer, we organize instrument prompts into a hierarchical prompt parsing tree with the instrument-shared prompt partition as the root node, n-part-shared prompt partitions as intermediate nodes and instrument-distinct prompt partitions as leaf nodes, to expose the reusable historical knowledge for new classes to simplify their learning. Conversely, to encourage positive backward knowledge transfer, we conduct self-reflection refining on existing knowledge by directed-weighted graph propagation, examining the knowledge associations recorded in the tree to improve its representativeness without causing catastrophic forgetting. Our framework is applicable to both CNN-based models and advanced transformer-based foundation models, yielding more than 5% and 11% improvements over the competing methods on two public benchmarks respectively.