44 citations · 52 across the 9 of their papers we have counts for
9 papers
PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models
Xianjing Cheng, Lintai Wu, Zuowen Wang +3
Accurate 3D scene understanding in outdoor environments heavily relies on high-quality point clouds. However, LiDAR-scanned data often suffer from extreme sparsity, severely hinder…
Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings
Yuanxi Wang, Zuowen Wang, Shih-Chii Liu
This paper presents an efficient deep learning solution for decoding motor movements from neural recordings in non-human primates. An Autoencoder Gated Recurrent Unit (AEGRU) model…
Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input
Joachim Ott, Zuowen Wang, Shih-Chii Liu
Event cameras are advantageous for tasks that require vision sensors with low-latency and sparse output responses. However, the development of deep network algorithms using event c…
Event-Based Eye Tracking. AIS 2024 Challenge Survey
Zuowen Wang, Chang Gao, Zongwei Wu +36
This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting t…
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Xi Chen, Chang Gao, Zuowen Wang +4
Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large…
3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
Qinyu Chen, Zuowen Wang, Shih-Chii Liu +1
This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technol…
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…
Understanding (Non-)Robust Feature Disentanglement and the Relationship Between Low- and High-Dimensional Adversarial Attacks
Zuowen Wang, Leo Horne
Recent work has put forth the hypothesis that adversarial vulnerabilities in neural networks are due to them overusing "non-robust features" inherent in the training data. We show…
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Fanny Yang, Zuowen Wang, Christina Heinze-Deml
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustn…