A spatiotemporal graph is built from raw events; its Laplacian eigenvectors, computed via a reordered matrix using an event-density prior, are used to filter noise events.
Converting static image datasets to spiking neuromorphic datasets using saccades
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.
citing papers explorer
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Denoising for Neuromorphic Cameras Based on Graph Spectral Features
A spatiotemporal graph is built from raw events; its Laplacian eigenvectors, computed via a reordered matrix using an event-density prior, are used to filter noise events.
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Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.