activity
20242026
collaborators

12 papers

cs.CV2026

Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms

Peipei Zhu, Yueqing Niu, Lin Zhu +3

Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex back…

cs.CV2026

Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking

Shiao Wang, Xiao Wang, Duoqing Yang +5

Despite significant progress, RGB-based trackers remain vulnerable to challenging imaging conditions, such as low illumination and fast motion. Event cameras offer a promising alte…

cs.CV2026

Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking

Shiao Wang, Xiao Wang, Haonan Zhao +6

Existing RGB-Event visual object tracking approaches primarily rely on conventional feature-level fusion, failing to fully exploit the unique advantages of event cameras. In partic…

cs.CV2025

Event Stream-based Sign Language Translation: A High-Definition Benchmark Dataset and A Novel Baseline

Shiao Wang, Xiao Wang, Duoqing Yang +5

Sign Language Translation (SLT) is a core task in the field of AI-assisted disability. Traditional SLT methods are typically based on visible light videos, which are easily affecte…

cs.CV2025

ESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning

Xiao Wang, Jingtao Jiang, Qiang Chen +5

Event stream based scene text recognition is a newly arising research topic in recent years which performs better than the widely used RGB cameras in extremely challenging scenario…

cs.CV2025

Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection

Xiao Wang, Yu Jin, Lan Chen +5

Event-based Vision Sensors (EVS) have demonstrated significant advantages over traditional RGB frame-based cameras in low-light conditions, high-speed motion capture, and low laten…