Publications (6)
RT-RMOT: A Dataset and Framework for RGB-Thermal Referring Multi-Object Tracking
Yanqiu Yu, Zhifan Jin, Sijia Chen +4
Referring Multi-Object Tracking has attracted increasing attention due to its human-friendly interactive characteristics, yet it exhibits limitations in low-visibility conditions,…
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
Sijia Chen, Yanqiu Yu, En Yu +1
Referring Multi-Object Tracking (RMOT) aims to track targets specified by language instructions. However, existing RMOT paradigms heavily rely on explicit visual-textual matching a…
ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking
Sijia Chen, Zihan Zhou, Yanqiu Yu +2
Multi-Object Tracking (MOT) is a fundamental task in computer vision, aiming to track targets across video frames. Existing MOT methods perform well in general visual scenes, but f…
DRMOT: A Dataset and Framework for RGBD Referring Multi-Object Tracking
Sijia Chen, Lijuan Ma, Yanqiu Yu +3
Referring Multi-Object Tracking (RMOT) aims to track specific targets based on language descriptions and is vital for interactive AI systems such as robotics and autonomous driving…
IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
Ye Tian, Yanqiu Yu, Liangliang Song +3
Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level pertur…
From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories
Ye Tian, Yanqiu Yu, Jianguo Sun +1
Malicious URLs persistently threaten the cybersecurity ecosystem, by either deceiving users into divulging private data or distributing harmful payloads to infiltrate host systems.…