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Xiaolong Zhou

7 accepted papers

2026

TP-Spikformer: Token Pruned Spiking Transformer

ICLR 2026poster

Spiking neural networks (SNNs) offer an energy-efficient alternative to traditional neural networks due to their event-driven computing paradigm. However, recent advancements in spiking transformers have focused on improving accuracy with large-scale architectures, which require significant computat…

Cited by 0SourceScholar
2025

Enhancing Nighttime Semantic Segmentation with Visual-Linguistic Priors and Wavelet Transform

IJCAI 2025

Nighttime semantic segmentation is a critical yet challenging task in autonomous driving. Most existing methods are designed for daytime scenarios, resulting in poor nighttime performance due to texture loss and decreased object visibility. Low-light enhancement was applied before segmentation but f

Cited by 0SourcePDFScholar
2025

SGFormer: Semantic-Geometry Fusion Transformer for Multi-modal 3D Panoptic Segmentation

AAAI 2025technical

Modern methods for autonomous driving perception widely adopt multi-modal fusion to enhance 3D scene understanding. However, existing methods suffer from inferior semantic extraction in image encoders that treat all pixels equally, ignoring contextual differences. The generated multi-modal represent…

Cited by 0SourcePDFScholar
2025

Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks

NeurIPS 2025poster

Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have greatly advanced, SNNs struggle to achieve competitive performance in visual long-sequence modeling task…

Cited by 0SourcecodeScholar