ReFu：用于无范例3D类增量学习的递归融合
ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning
AI总结：
提出ReFu模型，通过递归更新正则化自相关矩阵及点云引导的网格注意力层融合点云与网格特征，实现无范例3D类增量学习，有效缓解遗忘并优于现有方法。
摘要：
我们提出了一种名为ReFu的新型递归融合模型，旨在结合点云和网格以实现无范例3D类增量学习，模型在学习新3D类别的同时保留已学类别的知识。现有方法要么依赖存储历史数据来缓解遗忘，要么仅关注单一数据模态，与此不同，ReFu消除了范例存储的需求，同时利用点云和网格的互补优势。为此，我们引入一种递归方法，通过更新正则化自相关矩阵来持续积累知识。此外，我们提出一个融合模块，包含一个点云引导的网格注意力层，用于学习两种模态间的相关性。该机制有效融合点云与网格特征，实现更鲁棒且稳定的持续学习。跨多个数据集的实验表明，我们提出的框架在3D类增量学习中优于现有方法。
英文摘要：
We introduce a novel Recursive Fusion model, dubbed ReFu, designed to integrate point clouds and meshes for exemplar-free 3D Class-Incremental Learning, where the model learns new 3D classes while retaining knowledge of previously learned ones. Unlike existing methods that either rely on storing historical data to mitigate forgetting or focus on single data modalities, ReFu eliminates the need for exemplar storage while utilizing the complementary strengths of both point clouds and meshes. To achieve this, we introduce a recursive method which continuously accumulates knowledge by updating the regularized auto-correlation matrix. Furthermore, we propose a fusion module, featuring a Pointcloud-guided Mesh Attention Layer that learns correlations between the two modalities. This mechanism effectively integrates point cloud and mesh features, leading to more robust and stable continual learning. Experiments across various datasets demonstrate that our proposed framework outperforms existing methods in 3D class-incremental learning.
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。
