4 citations · 9 across the 14 of their papers we have counts for
7 papers · 1 filter
Event-based Motion Deblurring via Multi-Temporal Granularity Fusion
Xiaopeng Lin, Hongwei Ren, Yulong Huang +5
Conventional frame-based cameras inevitably produce blurry effects due to motion occurring during the exposure time. Event camera, a bio-inspired sensor offering continuous visual…
Scalable Event Cloud Network for Event-based Classification
Hongwei Ren, Fei Ma, Xiaopeng Lin +9
Event cameras are biologically inspired sensors garnering significant attention from both industry and academia. Mainstream methods favor frame and voxel representations, which rea…
PRF: Parallel Resonate and Fire Neuron for Long Sequence Learning in Spiking Neural Networks
Yulong Huang, Zunchang Liu, Changchun Feng +6
Recently, there is growing demand for effective and efficient long sequence modeling, with State Space Models (SSMs) proving to be effective for long sequence tasks. To further red…
FAPNet: An Effective Frequency Adaptive Point-based Eye Tracker
Xiaopeng Lin, Hongwei Ren, Bojun Cheng
Eye tracking is crucial for human-computer interaction in different domains. Conventional cameras encounter challenges such as power consumption and image quality during different…
Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba
Hongwei Ren, Yue Zhou, Jiadong Zhu +7
Event cameras draw inspiration from biological systems, boasting low latency and high dynamic range while consuming minimal power. The most current approach to processing Event Clo…
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…
CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Yulong Huang, Xiaopeng Lin, Hongwei Ren +5
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficien…