ECHOVIT: Vision Transformers Using Fast-And-Slow Time Embeddings-Reference-Cited by-同舟云学术

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ECHOVIT: Vision Transformers Using Fast-And-Slow Time Embeddings

Published:2023-07-16 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Ibikunle Oluwanisola¹,Varshney Debvrat²,Li Jilu¹,Rahnemoonfar Maryam³,Paden John¹

Affiliation:

1. University of Kansas,Center for Remote Sensing and Integrated Systems (CReSIS),Lawrence,KS,USA

2. University of Maryland Baltimore County,Department of Information Systems,Baltimore,MD,USA

3. Lehigh University,Department of Computer Science and Engineering,Bethlehem,PA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10281822.pdf?arnumber=10281822

Reference10 articles.

1. Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet

2. Deep multi-scale learning for automatic tracking of internal layers of ice in radar data

3. Attention is all you need;vaswani;Advances in neural information processing systems,2017

4. Annual Greenland accumulation rates (2009–2012) from airborne snow radar

5. Advanced Multifrequency Radar Instrumentation for Polar Research

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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