TU2.M3.5
HOW CAN MULTIMODAL REMOTE SENSING DATASETS TRANSFORM CLASSIFICATION VIA SPATIALNET-VIT?
Gautam Siddharth Kashyap, Macquarie University, Australia; Manaswi Kulahara, TERI School Of Advanced Studies, India; Nipun Joshi, Cornell University, United States; Usman Naseem, Macquarie University, Australia
Session:
TU2.M3: Deep Learning Techniques for Image Classification I Oral
Track:
AI and Big Data
Location:
Room M3
Presentation Time:
Tuesday, 5 August, 11:30 - 11:45
Presentation
Discussion
Resources
No resources available.
Session TU2.M3
TU2.M3.1: Dilated-RNNs: A Deep Approach for Continuous Volcano-Seismic Events Recognition
Manuel Titos, Joe Carthy, Luz García, University of Granada, Spain; Talfan Barnei, Icelandic Meteo Office, Iceland; Carmen Benítez, University of Granada, Spain
TU2.M3.2: COMMUNICATION-EFFICIENT FEDERATED LEARNING BASED ON EXPLANATION-GUIDED PRUNING FOR REMOTE SENSING IMAGE CLASSIFICATION
Jonas Klotz, Barış Büyüktaş, Begüm Demir, Technische Universität Berlin / Berlin Institute for the Foundations of Learning and Data, Germany
TU2.M3.3: DYNAMIC LEARNING RATE FOR FEW-SHOT INCREMENTAL LEARNING IN OPTICAL REMOTE SENSING IMAGE CLASSIFICATION
Yihang Liu, Mingyang Ma, Zonghao Han, Zixiang Ye, Huiyang Han, Shaohui Mei, Northwestern Polytechnical University, China
TU2.M3.4: Multi-Scale Attention and Contrastive Learning for Signal Modulation Classification
Hongbo Li, Yongyu Ge, Feiyu Yu, Youjia Guo, Jian Zhao, Harbin Institute of Technology, China
TU2.M3.5: HOW CAN MULTIMODAL REMOTE SENSING DATASETS TRANSFORM CLASSIFICATION VIA SPATIALNET-VIT?
Gautam Siddharth Kashyap, Macquarie University, Australia; Manaswi Kulahara, TERI School Of Advanced Studies, India; Nipun Joshi, Cornell University, United States; Usman Naseem, Macquarie University, Australia
Resources
No resources available.