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20242026
most citedScalable Event Cloud Network for Event-based Classification

1 citations · 1 across the 1 of their papers we have counts for

collaborators

7 papers

cs.CV20261 cited

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…

cs.CV2025

ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring

Xiaopeng Lin, Yulong Huang, Hongwei Ren +4

Motion deblurring addresses the challenge of image blur caused by camera or scene movement. Event cameras provide motion information that is encoded in the asynchronous event strea…

cs.CV2025

Exploring Temporal Dynamics in Event-based Eye Tracker

Hongwei Ren, Xiaopeng Lin, Hongxiang Huang +2

Eye-tracking is a vital technology for human-computer interaction, especially in wearable devices such as AR, VR, and XR. The realization of high-speed and high-precision eye-track…

cs.CV2025

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…

cs.NE2025

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…

cs.CV2024

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…