Simple Transformer with Single Leaky Neuron for Event Vision

Himanshu Kumar, Aniket Konkar; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 928-934

There is increasing interest in integrating the self-attention mechanism and transformer architecture in event-based vision. Several event-based and spiking neural network transformers have been proposed with complex architectures. We propose a simple transformer with a multi-head attention mechanism a feature extractor and a single leaky neuron. We have used Resnet an effective feature extractor for RGB images on frame-based event data. Experimental results show that our architecture surpasses many event and spiking-based methods including transformers and achieves competitive performance in DVS Gesture N-MNIST and CIFAR10-DVS datasets. Notably we achieve an accuracy of 98.3% on the DVS Gesture and 99.3% on the N-MNIST dataset.