102 citations · 197 across the 8 of their papers we have counts for
8 papers
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks
Yuhuang Hu, Shih-Chii Liu
Deep Neural Networks, particularly Convolutional Neural Networks (ConvNets), have achieved incredible success in many vision tasks, but they usually require millions of parameters…
Exploiting Spatial Sparsity for Event Cameras with Visual Transformers
Zuowen Wang, Yuhuang Hu, Shih-Chii Liu
Event cameras report local changes of brightness through an asynchronous stream of output events. Events are spatially sparse at pixel locations with little brightness variation. W…
T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events
Shu Wang, Yuhuang Hu, Shih-Chii Liu
Spiking silicon cochlea sensors encode sound as an asynchronous stream of spikes from different frequency channels. The lack of labeled training datasets for spiking cochleas makes…
v2e: From Video Frames to Realistic DVS Events
Yuhuang Hu, Shih-Chii Liu, Tobi Delbruck
To help meet the increasing need for dynamic vision sensor (DVS) event camera data, this paper proposes the v2e toolbox that generates realistic synthetic DVS events from intensity…
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
Yuhuang Hu, Jonathan Binas, Daniel Neil +2
Neuromorphic event cameras are useful for dynamic vision problems under difficult lighting conditions. To enable studies of using event cameras in automobile driving applications,…
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
Yuhuang Hu, Tobi Delbruck, Shih-Chii Liu
Novel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to u…
Overcoming the vanishing gradient problem in plain recurrent networks
Yuhuang Hu, Adrian Huber, Jithendar Anumula +1
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU)…
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu +1
Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time rema…