5 papers · 1 filter
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…
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…
When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models
Hui Lu, Yi Yu, Yiming Yang +5
Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single…