TU2.M1.2
Uncertainty-Guided Continuous Adaptation of Deep Learning Models in Dynamic Remote Sensing Environments
Mohammed El Amin LARABI, Meziane Iftene, Algerian Space Agency, Algeria; OMAR ALIKARA, Algerian Sapce Agency, Algeria
Session:
TU2.M1: Probabilistic Machine Learning for Earth Observation Oral
Track:
Community-Contributed Sessions
Location:
Mezzanine: Room M1
Presentation Time:
Tuesday, 5 August, 10:45 - 11:00
Session Chair:
Francisco Mena, University of Kaiserslautern-Landau
Session TU2.M1
TU2.M1.1: UA-VSTN: Uncertainty-aware Variational Spatiotemporal Network for Traffic State Prediction
Yaohong Ma, Yuxi Duan, Chao Zhang, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China
TU2.M1.2: Uncertainty-Guided Continuous Adaptation of Deep Learning Models in Dynamic Remote Sensing Environments
Mohammed El Amin LARABI, Meziane Iftene, Algerian Space Agency, Algeria; OMAR ALIKARA, Algerian Sapce Agency, Algeria
TU2.M1.3: Semi-Supervised Seabed Sediment Classification Based on Multi-level Contrastive Learning and Uncertainty Measurement
Haoyi Wang, Siyang Wan, Weitao Chen, Xianju Li, China University of Geosciences, Wuhan, China; Xuwen Qin, China Aero Geophysical Survey and Remote Sensing Centre for Natural Resources, China Geological Survey, China
TU2.M1.4: DUAL ENSEMBLE ENHANCED PHYSICAL ATTACKS ON AERIAL DETECTION
Yifan Liu, Northwestern Polytechnical University, China; Juan Xu, Chang’ an University, China; Shaohui Mei, Jiawei Lian, Xiaofei Wang, Yuru Su, Northwestern Polytechnical University, China
TU2.M1.5: REGDIFF: REGRESSION DIFFUSION FOR EARTH OBSERVATION
Miro Miranda, Ashutosh Dinesh, Duway Nicolas Lesmes-Leon, Francisco Mena, Marcela Charfuelan, Andreas Dengel, German Research Center for Artifical Intelligence, Germany
Resources