A Temporally Augmented Graph Attention Network for Affordance Classification
Summary
EEG-tGAT is a temporally augmented Graph Attention Network that improves affordance classification from interaction sequences by incorporating temporal attention and dropout mechanisms. The model enhances GATv2 for sequential data where temporal dimensions are semantically non-uniform.
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Paper page - A Temporally Augmented Graph Attention Network for Affordance Classification
Source: https://huggingface.co/papers/2604.10149
Abstract
EEG-tGAT enhances Graph Attention Networks by incorporating temporal attention and dropout mechanisms to improve affordance classification from interaction sequences.
Graph attention networks (https://huggingface.co/papers?q=Graph%20attention%20networks) (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 (https://huggingface.co/papers?q=Electroencephalography)-temporal Graph Attention Network (EEG-tGAT), a temporally augmented formulation of GATv2 (https://huggingface.co/papers?q=GATv2) that is tailored for affordance classification (https://huggingface.co/papers?q=affordance%20classification) from interaction sequences (https://huggingface.co/papers?q=interaction%20sequences). The proposed model incorporates temporal attention (https://huggingface.co/papers?q=temporal%20attention) to modulate the contribution of different time segments and temporal dropout (https://huggingface.co/papers?q=temporal%20dropout) 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 (https://huggingface.co/papers?q=GATv2). The observed gains help us conclude that explicitly encoding temporal importance and enforcing temporal robustness introduce inductive biases (https://huggingface.co/papers?q=inductive%20biases) that are much better aligned with the structure of affordance-driven interaction data. These findings show that modest architectural changes to graph attention models can help achieve consistent benefits when temporal relationships play a nontrivial role in the task.
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