3 accepted papers
Spiking Neural Networks (SNNs) enable event-driven computation with sparse activations, but building multimodal Transformers on SNNs is hindered by unstable training in deep spiking stacks and a mismatch between dense softmax attention and spike-based communication. We propose SMM Transformer, an SN…
Offline handwritten text recognition (HTR) is blank-dominated: task-relevant evidence lies in sparse ink strokes, yet mainstream recognizers still expend dense spatial compute and full-length width-axis token mixing across the canvas. Spiking neural networks (SNNs) promise activity-proportional comp…
Low-light image enhancement (LLIE) aims to improve the visibility and quality of images captured under poor illumination. However, existing deep enhancement methods often underemphasize computational efficiency, leading to high energy and memory costs. We propose \textbf{Spike-RetinexFormer}, a nove…