TU3.M3.4
Background-aware Prompt Learning for Remote Sensing Scene Classification
Wenxu Shi, Qingyan Meng, Linlin Zhang, Aerospace Information Research Institute, China; Tingyuan Zhou, Peter Atkinson, Lancaster University, United Kingdom
Session:
TU3.M3: Deep Learning Techniques for Image Classification II Oral
Track:
AI and Big Data
Location:
Room M3
Presentation Time:
Tuesday, 5 August, 14:00 - 14:15
Presentation
Discussion
Resources
No resources available.
Session TU3.M3
TU3.M3.1: A Height-Spatial Multi-Scale Feature Fusion Network for Remote Sensing Scene Classification
Jiadong Lin, Lingling Li, Xidian University, China; Huaji Zhou, the National Key Laboratory of Electromagnetic Space Security, China; Licheng Jiao, Fang Liu, Xu Liu, Xidian University, China
TU3.M3.2: LEVERAGING COVARIANCE REPRESENTATIONS WITH RIEMANNIAN RESNET FOR SATELLITE IMAGE TIME SERIES CLASSIFICATION
Khizer Zakir, Charlotte Pelletier, Université Bretagne Sud, France; Laetitia Chapel, L'Institut Agro Rennes-Angers, France; Nicolas Courty, Université Bretagne Sud, France
TU3.M3.3: EEMAMBA: A HARDWARE-AWARE ENERGY-EFFICIENT STATE-SPACE MODEL FOR EUROSAT CLASSIFICATION
Maneet Chatterjee, Indian Institute of Engineering Science and Technology Shibpur, India; Anuvab Sen, Georgia Institute of Technology, United States; Subhabrata Roy, Indian Institute of Engineering Science and Technology Shibpur, India
TU3.M3.4: Background-aware Prompt Learning for Remote Sensing Scene Classification
Wenxu Shi, Qingyan Meng, Linlin Zhang, Aerospace Information Research Institute, China; Tingyuan Zhou, Peter Atkinson, Lancaster University, United Kingdom
TU3.M3.5: ADDRESSING SEABED CHARACTERIZATION AS FUZZY DEEP LEARNING SEGMENTATION TO MITIGATE AMBIGUOUS SYNTHETIC APERTURE SONAR DATA
Yoann ARHANT, Olga Lopera Tellez, Xavier Neyt, Royal Military Academy, Belgium; Aleksandra Pizurica, Gent University, Belgium
Resources
No resources available.