Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most existing datasets predominantly focus on general o...
arXiv:2609.37003v1 Announce Type: new
Abstract: Vessel perception from space is crucial for a wide range of maritime applications, from traffic monitoring to environmental protection. However, most e...
By Danfeng Hong, Chenyu Li, Jocelyn Chanussot
arXiv:2608.29852v1 Announce Type: new
Abstract: Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-se...
By Amir Abbes, Ines Harrabi, Lucas Justin Yirepoa Kinda, Rim Trabelsi, Adnane Cabani, Fatma Abdelkefi
arXiv:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.
By Yuan Guan, Chandler Squires, Timothy Hu, Pradeep Ravikumar
arXiv:2607. 24453v1 Announce Type: cross Abstract: Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly.
By Mingzhi Xu, Yizhe Zhang
The paper presents a three‑stage, parameter‑efficient approach to improve automatic target recognition (ATR) with synthetic aperture sonar (SAS) data by adapting DINOv3 Vision Transformers. Stage 1 applies Low‑Rank Adaptation (LoRA) while freezing the backbone, which significantly boosts the area under the precision‑recall curve from 0.300 to 0.679. Subsequent hard‑negative mining and supervised contrastive learning stages show negligible impact, indicating that a single LoRA adaptation is sufficient for effective underwater ATR.
By Dan Zimmerman, Frank E. Bobe III, Amelia L. McCormack, Matthew Cook, Gregory D. Vetaw