面向可供性分类的时间增强图注意力网络

摘要

图注意力网络（GATs）为关系数据中的节点表示学习提供了最优框架之一；然而，现有变体如经典图注意力网络主要处理静态图结构，在应用于序列数据时依赖于隐式的时间聚合。本文提出脑电时序图注意力网络（EEG-tGAT），这是一种针对交互序列中可供性分类任务优化的GATv2时序增强架构。该模型通过时序注意力机制调节不同时间片段的贡献度，并采用时序丢弃技术对时间相关观测值进行正则化处理。该设计基于以下假设：可供性数据中的时间维度在语义上具有非均匀性，且判别性信息可能随时间呈不均衡分布。在可供性数据集上的实验表明，EEG-tGAT相比GATv2实现了分类性能的提升。这一增益证实：显式编码时间重要性并增强时序鲁棒性所引入的归纳偏置，能更好地契合可供性驱动交互数据的结构特征。这些发现表明，当时间关系在任务中起关键作用时，对图注意力模型进行适度的架构调整可带来持续的性能增益。

English

Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such as Graph Attention Network (GAT) mainly operate on static graphs and rely on implicit temporal aggregation when applied to sequential data. In this paper, we introduce Electroencephalography-temporal Graph Attention Network (EEG-tGAT), a temporally augmented formulation of GATv2 that is tailored for affordance classification from interaction sequences. The proposed model incorporates temporal attention to modulate the contribution of different time segments and temporal dropout to regularize learning across temporally correlated observations. The design reflects the assumption that temporal dimensions in affordance data are not semantically uniform and that discriminative information may be unevenly distributed across time. Experimental results on affordance datasets show that EEG-tGAT achieves improved classification performance compared to GATv2. The observed gains helps to conclude that explicitly encoding temporal importance and enforcing temporal robustness introduce inductive biases that are much better aligned with the structure of affordance-driven interaction data. These findings show us that modest architectural changes to graph attention models can help one obtain consistent benefits when temporal relationships play a nontrivial role in the task.

面向可供性分类的时间增强图注意力网络

A Temporally Augmented Graph Attention Network for Affordance Classification

摘要

Support