173 citations · 854 across the 53 of their papers we have counts for
8 papers · 1 filter
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
Dengyu Wu, Gaojie Jin, Han Yu +2
Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvem…
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
Kaiwen Cai, Chris Xiaoxuan Lu, Xiaowei Huang
Place recognition is key to Simultaneous Localization and Mapping (SLAM) and spatial perception. However, a place recognition in the wild often suffers from erroneous predictions d…
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Yanda Meng, Hongrun Zhang, Dongxu Gao +5
Segmentation is an essential operation of image processing. The convolution operation suffers from a limited receptive field, while global modelling is fundamental to segmentation…
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Yanda Meng, Hongrun Zhang, Yitian Zhao +4
Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dens…
A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Network
Dengyu Wu, Xinping Yi, Xiaowei Huang
Spiking neural networks (SNNs) offer an inherent ability to process spatial-temporal data, or in other words, realworld sensory data, but suffer from the difficulty of training hig…
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
Yanghao Zhang, Wenjie Ruan, Fu Wang +1
The previous study has shown that universal adversarial attacks can fool deep neural networks over a large set of input images with a single human-invisible perturbation. However,…
Reliability Validation of Learning Enabled Vehicle Tracking
Youcheng Sun, Yifan Zhou, Simon Maskell +2
This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The s…
Feature-Guided Black-Box Safety Testing of Deep Neural Networks
Matthew Wicker, Xiaowei Huang, Marta Kwiatkowska
Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. Most existing approaches for crafting adversarial e…