用双曲几何保持多模态学习中知识的层次结构，防止遗忘。
Hyperbolic Multimodal Continual Learning
- 基于双曲等距变换，实现跨模态关系与层级结构的联合保持。
- 在多模态持续学习中，显著降低语义关系漂移和层次失真。
- 适合研究多模态表示学习与持续学习交叉方向的学者。
双曲几何最近被证明是多模态学习的强大表示空间，因其能自然捕捉跨模态的层次语义结构。尽管如此，此类表示在持续学习中的行为仍面临根本性挑战，尚未充分探索。本文从几何角度分析该问题，建立了双曲空间中表示保留的理论基础，表明防止遗忘需满足跨模态在共享双曲等距下的不变性。进一步揭示，双曲持续学习中的遗忘包含语义关系漂移与层次相关畸变，因此需同时保持跨模态关系结构与层级几何。基于此，提出一个遵循几何原理的持续学习框架，在保留关键几何结构的同时支持对新任务的有效适应。在多个持续多模态基准上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Hyperbolic geometry has recently emerged as a powerful representation space for multimodal learning, as it naturally captures hierarchical semantic structure across modalities. Despite this progress, how such representations behave under continual learning poses fundamentally different challenges that remain underexplored. This work provides a geometric perspective on this problem and establishes a theoretical foundation for representation preservation in hyperbolic space, showing that preventing forgetting requires cross-modal invariance under a shared hyperbolic isometry. We further show that forgetting in hyperbolic continual learning involves both semantic relation drift and hierarchy-related distortion, motivating preservation of both cross-modal relational structure and hierarchical geometry. Guided by these insights, a principled continual learning framework is derived that preserves essential geometric structure while allowing effective adaptation to new tasks. Experiments on continual multimodal benchmarks corroborate the effectiveness of the proposed approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。