TU3.R12.4
VISION-LANGUAGE MODEL GUIDED PSEUDO-LABEL PURIFICATION FOR CROSS-DOMAIN SAR SHIP DETECTION
Jiahao Li, Zongyong Cui, Zheng Zhou, Yijun Li, Zongjie Cao, University of Electronic Science and Technology of China, China
Session:
TU3.R12: SAR Ship Detection Oral
Track:
Theory and Techniques
Location:
TBD
Presentation Time:
Tue, 11 Aug, 14:30 - 14:45
Presentation
Discussion
Resources
No resources available.
Session TU3.R12
TU3.R12.1: Ship Detection in SAR images with Multimodal Feature Fusion
Zirui Li, Jiashu Zheng, University of Electronic Science and Technology of China, China; Jiming Li, Sichuan University, China
TU3.R12.2: ENHANCING SAR SHIP DETECTION: FREQUENCY-DOMAIN FEATURES MEET SCALE-SENSITIVE LOSS
Xuelian Xu, Xin Su, Yang Fang, Huiping Lin, Chongqing University, China; Zongsi Chen, Fudan University, China
TU3.R12.3: PHYSICS-AWARE OPEN-SET SAR SHIP DETECTION VIA CFAR-GUIDED LOCAL CONTRAST AND PROBABILISTIC ENERGY FUSION
Tiancheng Ai, Zongyong Cui, Zheng Zhou, Jiahao Li, Zongjie Cao, University of Electronic Science and Technology of China, China
TU3.R12.4: VISION-LANGUAGE MODEL GUIDED PSEUDO-LABEL PURIFICATION FOR CROSS-DOMAIN SAR SHIP DETECTION
Jiahao Li, Zongyong Cui, Zheng Zhou, Yijun Li, Zongjie Cao, University of Electronic Science and Technology of China, China
TU3.R12.5: Feasibility of Lightweight CNN-Based Ship Detection with the Geostationary Ocean Color Imager-II (GOCI-II): Applications to the Yellow Sea
Won-Kyung Baek, YeongJae Jang, Donguk Lee, Sung-Hwan Park, Joo-Hyung Ryu, Korea Institute of Ocean Science and Technology, Korea (South)
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