MCIGLE：多模态无样本类增量图学习
MCIGLE: Multimodal Exemplar-Free Class-Incremental Graph Learning
AI总结：
针对多模态图数据上的无样本类增量学习，提出MCIGLE框架，通过多模态特征提取与对齐及拼接递归最小二乘法实现知识保留，平衡准确性与内存，实验验证其有效性和泛化性。
摘要：
无样本类增量学习使模型能够随时间学习新类别，而无需存储旧类别的数据。随着多模态图结构数据日益普遍，现有方法在处理灾难性遗忘、分布偏差、内存限制和泛化能力弱等挑战时面临困难。我们提出MCIGLE，一种新颖的框架，通过提取和对齐多模态图特征，并应用拼接递归最小二乘法实现有效的知识保留，来解决这些问题。通过多通道处理，MCIGLE在准确性和内存保留之间取得平衡。在公共数据集上的实验验证了其有效性和泛化能力。
英文摘要：
Exemplar-free class-incremental learning enables models to learn new classes over time without storing data from old ones. As multimodal graph-structured data becomes increasingly prevalent, existing methods struggle with challenges like catastrophic forgetting, distribution bias, memory limits, and weak generalization. We propose MCIGLE, a novel framework that addresses these issues by extracting and aligning multimodal graph features and applying Concatenated Recursive Least Squares for effective knowledge retention. Through multi-channel processing, MCIGLE balances accuracy and memory preservation. Experiments on public datasets validate its effectiveness and generalizability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
