Abstract
In recent years, various methods have been proposed for mesh analysis, each offering distinct advantages and often excelling on different object classes. We present a novel Mixture of Experts (MoE) framework designed to harness the complementary strengths of these diverse approaches. We propose a new gate architecture that encourages each expert to specialise in the classes it excels in. Our design is guided by two key ideas: (1) random walks over the mesh surface effectively capture the regions that individual experts attend to, and (2) an attention mechanism that enables the gate to focus on the areas most informative for each expert's decision-making. To further enhance performance, we introduce a dynamic loss balancing scheme that adjusts a trade-off between diversity and similarity losses throughout the training, where diversity prompts expert specialization, and similarity enables knowledge sharing among the experts. Our framework achieves state-of-the-art results in mesh classification, retrieval, and semantic segmentation tasks. Our code is available at: this https URL.
Abstract (translated)
近年来，人们提出了多种网格分析方法，每种方法都具有独特的优势，并且在不同的物体类别上表现出色。我们提出了一种新颖的专家混合（MoE）框架，旨在利用这些不同方法的优势。我们设计了一个新的门控架构，鼓励每个专家专注于其擅长的对象类别。我们的设计基于两个关键理念：(1) 网格表面上的随机游走可以有效地捕捉各个专家关注的区域；(2) 一种注意力机制能够使门控聚焦于对每位专家决策最有信息量的区域。为了进一步提高性能，我们引入了一种动态损失平衡方案，在整个训练过程中调整多样性和相似性损失之间的权衡，其中多样性推动了专家的专业化，而相似性则促进了专家间的知识共享。我们的框架在网格分类、检索和语义分割任务中达到了最先进的成果。我们的代码可在以下链接获取：this https URL.
URL
https://arxiv.org/abs/2603.00828
