TH4.R6.5

SELF-DETECTION FINE-TUNING: A FRAMEWORK FOR ENHANCING THE PERFORMANCE OF LEARNING-BASED DEBLURRING MODELS ON UAV DATA

Weitao Yue, Xiaowei Zhao, University of Warwick, United Kingdom
Session:
TH4.R6: Self-supervised Learning Oral
Track:
AI and Big Data
Location:
Skalkotas Hall
Presentation Time:
Thu, 11 Jul, 18:16 - 18:30
Session Co-Chairs:
Xiao Xiang Zhu, Technical University of Munich and Ioannis Papoutsis , NOA
Presentation
Discussion
Resources
No resources available.
Session TH4.R6
TH4.R6.1: LESS IS MORE: ACTIVE SELF-SUPERVISED LEARNING IN REMOTE SENSING
Xuemei Jiang, Linus Scheibenreif, Damian Borth, University of St.Gallen, Switzerland
TH4.R6.2: ADVANCING MULTI-SCALE REMOTE SENSING ANALYSIS THROUGH SELF-SUPERVISED LEARNING FINE-TUNING STRATEGIES
Konstantinos Georgiou, Maofeng Tang, Fanqi Wang, Hairong Qi, University of Tennessee, Knoxville, United States; Marc Bosch Ruiz, Cody Champion, Accenture Federal Service, United States
TH4.R6.3: CONTRASTIVE LEARNING FOR RADAR TARGET RECOGNITION BASED ON HRRP
Hao Wan, Peikun Zhu, Jing Liang, University of Electronic Science and Technology of China, China
TH4.R6.4: TASK SPECIFIC PRETRAINING WITH NOISY LABELS FOR REMOTE SENSING IMAGE SEGMENTATION
Chenying Liu, Technical University of Munich, German Aerospace Center, Germany; Conrad Albrecht, German Aerospace Center, Germany; Yi Wang, Xiao Xiang Zhu, Technical University of Munich, Germany
TH4.R6.5: SELF-DETECTION FINE-TUNING: A FRAMEWORK FOR ENHANCING THE PERFORMANCE OF LEARNING-BASED DEBLURRING MODELS ON UAV DATA
Weitao Yue, Xiaowei Zhao, University of Warwick, United Kingdom
TH4.R6.6: A SELF-SUPERVISED APPROACH FOR PRODUCING A LAND USE MAP FOR SOUTHERN QUEBEC
Étienne Clabaut, Samuel Foucher, Yacine Bouroubi, Mickaël Germain, Université de Sherbrooke, Canada
TH4.R6.7: TOWARDS CLEARER MARS IMAGES: SELF-SUPERVISED DENOISING WITH LARGE VISION MODEL
Jiawei Wang, Hui Tian, Junjie Li, Wentao Hu, Xinyuan Li, Weijie Yue, Beijing University of Posts and Telecommunication, China
Resources
No resources available.