发表机构
University of Science and Technology Beijing; Beijing University of Posts and Telecommunications; Institute of Automation, Chinese Academy of Sciences; Singapore Management University（北京科技大学; 北京邮电大学; 中国科学院自动化研究所; 新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对基础数据稀缺的泛化小样本类增量学习，提出利用潜在扩散模型生成合成数据，并通过知识策展策略选择高质量样本，结合边界稳定适应方案，有效缓解语义漂移和类别冲突。
AI 中文摘要
小样本类增量学习（FSCIL）旨在从有限的标注中学习新类别，同时保留先验知识。现有方法通常假设基础会话规模足够大，但当基础数据和增量数据都稀缺时，这一假设不成立，导致初始表示薄弱、语义漂移和边界不稳定。我们研究这一未被充分探索但现实存在的设置，称为泛化FSCIL（G-FSCIL），其中基础会话本身仅包含少数几个类别。尽管合成数据可以缓解监督稀缺问题，但天真地混合生成样本往往会引入语义噪声并加剧新旧类别边界冲突。为解决此问题，我们提出一个框架，为稳定的G-FSCIL策展可信的合成知识。具体而言，我们首先使用冻结的潜在扩散模型构建类别特定的合成候选池，在首次观察时执行类别反转，并将所得条件嵌入重用用于按需生成。基于这些候选，我们学习一种知识策展策略，选择具有语义一致性和视觉多样性的样本，并在基础会话期间将此过程蒸馏为可迁移的选择策略，之后无需进一步优化即可重用。利用策展的合成数据，我们进一步设计了一种边界稳定的增量适应方案，包括合成信息原型初始化和双向边界校准以缓解新旧冲突。大量实验表明，我们的方法持续优于现有FSCIL基线，减少了遗忘并改善了新旧类别之间的平衡。代码可在https URL获取。
英文摘要
Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typically assume a sufficiently large base session, but this assumption fails when both base and incremental data are scarce, leading to weak initial representations, semantic drift, and unstable boundaries. We study this underexplored yet realistic setting, termed Generalizable FSCIL (G-FSCIL), where the base session itself contains only a few classes. Although synthetic data can alleviate supervision scarcity, naively mixing generated samples often introduces semantic noise and exacerbates old-new boundary conflicts. To address this, we propose a framework that curates trustworthy synthetic knowledge for stable G-FSCIL. Specifically, we first construct class-specific synthetic candidate pools using a frozen latent diffusion model, where class inversion is performed at the first observation and the resulting condition embeddings are reused for on-demand generation. Building on these candidates, we learn a knowledge curation strategy that selects samples with both semantic consistency and visual diversity, and distill this process into a transferable selection policy during the base session, which is then reused without further optimization. Leveraging the curated synthetic data, we further design a boundary-stable incremental adaptation scheme, including synthetic-informed prototype initialization and bidirectional boundary calibration to mitigate old-new conflicts. Extensive experiments demonstrate that our method consistently outperforms existing FSCIL baselines, with reduced forgetting and improved balance between old and new classes. Code is available at https://github.com/NiHaoWoJiaoYYC/G-FSCIL.
CommentsAccepted by ACM MM 2026
