Mesh representation by random walks has been shown to benefit deep learning. Randomness is indeed a powerful concept. However, it comes with a price: some walks might wander around non-characteristic regions of the mesh, which might be harmful to shape analysis, especially when only a few walks are utilized. We propose a novel walk-attention mechanism that leverages the fact that multiple walks are used. The key idea is that the walks may provide each other with information regarding the meaningful (attentive) features of the mesh. We utilize this mutual information to extract a single descriptor of the mesh. This differs from common attention mechanisms that use attention to improve the representation of each individual descriptor. Our approach achieves SOTA results for two basic 3D shape analysis tasks: classification and retrieval. Even a handful of walks along a mesh suffice for learning.
翻译:随机行走的流体代表方式被证明有利于深层学习。 随机性确实是一个强大的概念。 但是,它带来一个代价: 一些行走可能会在网格中非特征性区域游荡, 这可能有害于分析的形状, 特别是在只使用几条行走的情况下。 我们提议了一个新型的亲身体验机制, 藉以利用多行走这一事实。 关键的想法是行走可以互相提供有关网格中有意义的( 注意) 特征的信息。 我们利用这种相互信息来提取网格的单一描述符。 这与使用关注来改善每个个体标语代表的普通关注机制不同。 我们的方法在两个基本的 3D 形状分析任务中取得了SOTA结果: 分类和检索。 即使是几个沿着网格行走的行走也足以学习。