3 papers
cs.LG2026
Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies
Youngseok Hwang, Sungho Bae, Dohun Lee +4
Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as…
cs.LG2025
Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
Jeehong Kim, Youngseok Hwang, Minchan Kim +2
Spatio-temporal graph neural networks (ST-GNNs) have achieved notable success in structured domains such as road traffic and public transportation, where spatial entities can be na…
cs.LG2025
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
Jeehong Kim, Minchan Kim, Jaeseong Ju +3
Graph neural networks have emerged as a powerful tool for learning spatiotemporal interactions. However, conventional approaches often rely on predefined graphs, which may obscure…