8 papers
General Self-Prediction Enhancement for Spiking Neurons
Zihan Huang, Zijie Xu, Yihan Huang +7
Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs a…
Unleashing Temporal Capacity of Spiking Neural Networks through Spatiotemporal Separation
Yiting Dong, Zhaofei Yu, Jianhao Ding +2
Spiking Neural Networks (SNNs) are considered naturally suited for temporal processing, with membrane potential propagation widely regarded as the core temporal modeling mechanism.…
Driving in Spikes: An Entropy-Guided Object Detector for Spike Cameras
Ziyan Liu, Qi Su, Lulu Tang +2
Object detection in autonomous driving suffers from motion blur and saturation under fast motion and extreme lighting. Spike cameras, offer microsecond latency and ultra high dynam…
HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking
Yao Deng, Xian Zhong, Wenxuan Liu +3
RGB cameras excel at capturing rich texture details with high spatial resolution, whereas event cameras offer exceptional temporal resolution and a high dynamic range (HDR). Levera…
: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
Kang Chen, Zhihao Liu, Tonghe Zhang +11
Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to…
SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos
Wenxuan Liu, Yao Deng, Kang Chen +3
Existing saliency detection methods struggle in real-world scenarios due to motion blur and occlusions. In contrast, spike cameras, with their high temporal resolution, significant…