8 papers
STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation
Shuhan Ye, Yi Yu, Qixin Zhang +5
SNNs promise energy-efficient and low-latency inference, but their performance still trails that of ANNs. ANN-to-SNN knowledge distillation helps narrow this gap, yet the original…
Multinoulli Extension: A Lossless Continuous Relaxation for Partition-Constrained Subset Selection
Qixin Zhang, Wei Huang, Yan Sun +3
Identifying the most representative subset for a close-to-submodular objective while satisfying the predefined partition constraint is a fundamental task with numerous applications…
Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks
Yi Yu, Qixin Zhang, Shuhan Ye +6
Spiking neural networks (SNNs) compute with discrete spikes and exploit temporal structure, yet most adversarial attacks change intensities or event counts instead of timing. We st…
Fire on Motion: Optimizing Video Pass-bands for Efficient Spiking Action Recognition
Shuhan Ye, Yuanbin Qian, Yi Yu +5
Spiking neural networks (SNNs) have gained traction in vision due to their energy efficiency, bio-plausibility, and inherent temporal processing. Yet, despite this temporal capacit…
Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillation
Shuhan Ye, Yi Yu, Qixin Zhang +4
Event cameras sense brightness changes and output binary asynchronous event streams, attracting increasing attention. Their bio-inspired dynamics align well with spiking neural net…
Breaking the Modality Wall: Time-step Mixup for Efficient Spiking Knowledge Transfer from Static to Event Domain
Yuqi Xie, Shuhan Ye, Yi Yu +7
The integration of event cameras and spiking neural networks (SNNs) promises energy-efficient visual intelligence, yet scarce event data and the sparsity of DVS outputs hinder effe…
Sparse by Rule: Probability-Based N:M Pruning for Spiking Neural Networks
Shuhan Ye, Yi Yu, Qixin Zhang +4
Brain-inspired Spiking neural networks (SNNs) promise energy-efficient intelligence via event-driven, sparse computation, but deeper architectures inflate parameters and computatio…
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection
Guangyu Ren, Yinxiao Yu, Hengyan Liu +1
RGB-D salient object detection (SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data. Inevitably, a…