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Neuromorphic vision sensors (event cameras) simulate biological visualperception systems and have the advantages of high temporal resolution, lessdata redundancy, low power consumption, and large dynamic range. Since bothevents and spikes are modeled from neural signals, event cameras are inherentlysuitable for spiking neural networks (SNNs), which are considered promisingmodels for artificial intelligence (AI) and theoretical neuroscience. However,the unconventional visual signals of these cameras pose a great challenge tothe robustness of spiking neural networks. In this paper, we propose a noveldata augmentation method, ViewPoint Transform and SpatioTemporal Stretching(VPT-STS). It improves the robustness of SNNs by transforming the rotationcenters and angles in the spatiotemporal domain to generate samples fromdifferent viewpoints. Furthermore, we introduce the spatiotemporal stretchingto avoid potential information loss in viewpoint transformation. Extensiveexperiments on prevailing neuromorphic datasets demonstrate that VPT-STS isbroadly effective on multi-event representations and significantly outperformspure spatial geometric transformations. Notably, the SNNs model with VPT-STSachieves a state-of-the-art accuracy of 84.4\% on the DVS-CIFAR10 dataset.

Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching