A Temporally Augmented Graph Attention Network for Affordance Classification

TL;DR

This paper introduces EEG-tGAT, a temporally augmented graph attention network designed for affordance classification from interaction sequences, improving performance by explicitly modeling temporal importance.

Contribution

The paper proposes EEG-tGAT, a novel temporal extension of GATv2, tailored for sequential data, incorporating temporal attention and dropout for better affordance classification.

Findings

EEG-tGAT outperforms GATv2 on affordance datasets.

Explicit temporal encoding improves classification accuracy.

Temporal regularization enhances model robustness.

Abstract

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.