4 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…
Action recognition in videos poses a challenge due to its high computational cost, especially for Joint Space-Time video transformers (Joint VT). Despite their effectiveness, the excessive number of tokens in such architectures significantly limits their efficiency. In this paper, we propose Halting…
Multi-view feature learning has garnered much attention recently since many real world data are comprised of different representations or views. How to explore the consensus structure and eliminate the inconsistency noise in different views remains a challenging problem in multi-view feature learnin…