English(EN) RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings

RelFlexformers引入高效3D-Transformer注意力与新颖位置编码

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-11 15:20

研究人员推出RelFlexformers，一类新颖的3D-Transformer模型，其利用高效注意力机制与可积分相对位置编码。这些模型在长度为L的输入序列上实现了O(L log L)的注意力计算时间复杂度。通过构建在非均匀傅里叶变换理论之上，RelFlexformers可以将现有的高效注意力方法泛化到非结构化和异构场景，使其适用于点云建模等任务。在各种3D数据集上的实证评估表明，这些新的注意力调制技术带来了质量上的提升。 AI

影响引入了一类新的高效3D-Transformer，可应用于点云建模，有望提升复杂空间数据的性能。

排序理由该集群包含一篇详细介绍新颖模型架构和注意力机制的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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RelFlexformers引入高效3D-Transformer注意力与新颖位置编码

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arXiv cs.LG TIER_1 English(EN) · Krzysztof Choromanski · 2026-05-11 15:20

RelFlexformer：用于可积分相对位置编码的高效注意力三维 Transformer

We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable modulation functions $f$. They lead to the new class of 3D-Transformer models, called \textit{RelFlexformers}, flexibly integrat…

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RelFlexformer：用于可积分相对位置编码的高效注意力三维 Transformer

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