13 papers
CertVLA: Certified Defense against Physical Visual Attacks for Vision-Language-Action Models
Hui Lu, Zhijie Peng, Yuqi Lin +8
Vision-Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify c…
ENCORE: Event-Assisted Complementary Motion Refinement for Learned Video Compression
Shuhan Ye, Hongbin Yu, Chenqi Kong +4
Learned video compression relies on accurate temporal modeling to remove redundancy between adjacent frames. However, most existing codecs infer motion solely from discretely sampl…
ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization
Jiaming He, Jiashu Zhang, Guanyu Hou +4
Text-to-video (T2V) diffusion models have made it increasingly easy to synthesize realistic and temporally coherent videos, while recent personalization techniques allow such model…
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…
Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects
Hudi He, Fukun Wang, Zhe Wang +7
Evaluating fairness in Spiking Neural Networks (SNNs) demands rigorous benchmarks that reflect real-world complexities, yet existing assessments remain limited by superficial datas…
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…
Time-step Mixup for Efficient Spiking Knowledge Transfer from Appearance to Event Domain
Yuqi Xie, Shuhan Ye, Yi Yu +7
The integration of event cameras and spiking neural networks holds great promise for energy-efficient visual processing. However, the limited availability of event data and the spa…
Cross Knowledge Distillation between Artificial and Spiking Neural Networks
Shuhan Ye, Yuanbin Qian, Chong Wang +4
Recently, Spiking Neural Networks (SNNs) have demonstrated rich potential in computer vision domain due to their high biological plausibility, event-driven characteristic and energ…
UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks
Yuanbin Qian, Shuhan Ye, Chong Wang +3
Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model's anomaly recognition ability, previous works have typically involved RGB, op…