TU3.P11.1
A DEEP MULTITASK LEARNING FRAMEWORK FOR MULTI-LEVEL LAND COVER CLASSIFICATION
shilin tao, Haoyu Fu, Southwest Forestry University, China; Ruiqi Yang, Yunnan Normal University, China; Leiguang Wang, Southwest Forestry University, China
Session:
TU3.P11: Interpretable Segmentation Models and Optimized Training Strategies Oral
Track:
Data Analysis
Location:
Room P11
Presentation Time:
Tuesday, 5 August, 13:15 - 13:30
Presentation
Discussion
Resources
No resources available.
Session TU3.P11
TU3.P11.1: A DEEP MULTITASK LEARNING FRAMEWORK FOR MULTI-LEVEL LAND COVER CLASSIFICATION
shilin tao, Haoyu Fu, Southwest Forestry University, China; Ruiqi Yang, Yunnan Normal University, China; Leiguang Wang, Southwest Forestry University, China
TU3.P11.2: Weighted Context: A Global Interpretation Method for Image Segmentation Models
Conor O'Sullivan, Soumyabrata Dev, University College Dublin, Ireland
TU3.P11.3: A Prior Feature-injected Cross-Attention auto-Encoder for Land Cover Segmentation using Optical and SAR Images
Qian Guo, Xinyu Liu, Nanjing University of Aeronautics and Astronautics, China; Sijia Lu, Nanjing Research Institute of Electronics Technology, China; Hui Bi, Nanjing University of Aeronautics and Astronautics, China
TU3.P11.4: SOURCE-FREE DOMAIN ADAPTATION FOR REMOTE SENSING IMAGE SEGMENTATION VIA TARGET DOMAIN DATA ENHANCEMENT
Junwei Dai, Yanyu Ye, Lei Zhang, Wei Wei, Northwestern Polytechnical University, China
TU3.P11.5: Dual-Branch Asymmetric Network for Arbitrary-Modal Segmentation of Remote Sensing Images
Zhengyi Xu, Jie Geng, Wen Jiang, Northwestern Polytechnical University, China
Resources
No resources available.