Ronghui Zhang

TA-NET: Empowering Highly Efficient Traffic Anomaly Detection Through Multi-Head Local Self-Attention and Adaptive Hierarchical Feature Reconstruction

2024 · IEEE Transactions on Intelligent Transportation Systems

In the realm of road surveillance systems, Automatic Incident Detection (AID) methods have shown promise in swiftly and precisely detecting traffic anomalies. Nevertheless, the paucity of frame-level precisely annotated training data poses substantial challenges. To …

GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder

2024 · arXiv (Cornell University)

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Domain Adaptation (UDA) methods often fall short in …