发表机构
IIIT Delhi; Microsoft; IIT Kanpur; TYUST（印度信息技术学院德里校区; 微软公司; 印度理工学院坎普尔校区; 太原科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对类增量语义分割的灾难性遗忘与背景偏移问题，提出SELECT选择性上下文迁移方法，通过上下文迁移注意力机制等提升性能，在Pascal VOC和ADE20K上优于现有工作。
AI 中文摘要
类增量语义分割（CISS）面临灾难性遗忘和背景偏移的根本挑战，学习新概念会降低对先前已见类别的性能。现有方法试图在稳定性（保留旧知识）和可塑性（学习新知识）之间取得平衡，但往往无法有效利用先验知识，这些方法通常依赖无差别知识迁移或模糊初始化，可能会稀释关键语义信息。为克服这一局限，本文提出SELECT，一种用于选择性上下文迁移的新方法，它将每个新类别基于一小部分语义相似的旧类别。其核心是上下文迁移注意力机制，该机制将相似类别学到的token聚合为新类别的结构化初始化。为确保此迁移不会损坏借用的表示，本文添加了受控噪声扰动和基于间隔的上下文迁移损失，以强制新类别token与其源token分离。在Pascal VOC和ADE20K上的大量实验表明，SELECT始终优于现有工作，在VOC上实现了2.2%的mIoU，在ADE上实现了2.8%的mIoU，为解决稳定性-可塑性困境提供了有效手段。代码可在指定的URL获取。
英文摘要
Class-Incremental Semantic Segmentation (CISS) is fundamentally challenged by catastrophic forgetting and background shift, where learning new concepts degrades performance on previously seen classes. While existing methods attempt to balance stability (retaining old knowledge) and plasticity (learning new knowledge), they often fail to leverage prior knowledge effectively. These approaches typically rely on indiscriminate knowledge transfer or ambiguous initializations, which can dilute crucial semantic information. To overcome this limitation, we propose SELECT, a novel approach for Selective Context Transfer, which instead grounds each new class in a small set of semantically similar past classes. Its core is a Context Transfer Attention mechanism that aggregates the learned tokens from similar classes into a structured initialization for the new class. To ensure this transfer does not corrupt the borrowed representations, we add a controlled noise perturbation and a margin-based context-transfer loss that enforces separation between the new class token and its source tokens. Extensive experiments on Pascal VOC and ADE20K show that SELECT consistently outperforms prior work, achieving mIoU of 2.2% on VOC and 2.8% on ADE, providing an effective handle on the stability-plasticity dilemma. Code is available at https://github.com/avigupta2798/SELECT.
CommentsAccepted to BMVC 2026
