activity
20182021
most citedTemporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving

11 citations · 19 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Spectrum Attention Mechanism for Time Series Classification

Shibo Zhou, Yu Pan

Time series classification(TSC) has always been an important and challenging research task. With the wide application of deep learning, more and more researchers use deep learning…

cs.CV20218 cited

A Spike Learning System for Event-driven Object Recognition

Shibo Zhou, Wei Wang, Xiaohua Li +1

Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found increased attention in high-resolution and high-speed applications. A lot of work has been conducted t…

cs.LG2020

Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection

Shibo Zhou, Xiaohua Li

Spiking neural network (SNN) is interesting due to its strong bio-plausibility and high energy efficiency. However, its performance is falling far behind conventional deep neural n…

cs.CV202011 cited

Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving

Wei Wang, Shibo Zhou, Jingxi Li +3

Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been succe…

cs.CV2019

Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data

Shibo Zhou, Ying Chen, Xiaohua Li +1

Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Most existing computer vision approaches are based on convoluti…

cs.NE2018

Object Detection based on LIDAR Temporal Pulses using Spiking Neural Networks

Shibo Zhou, Wei Wang

Neural networks has been successfully used in the processing of Lidar data, especially in the scenario of autonomous driving. However, existing methods heavily rely on pre-processi…