A Deep Contrastive Learning Approach to Extremely-Sparse Disaster Damage Assessment in Social Sensing

Yang Zhang; Ruohan Zong; Lanyu Shang; Ziyi Kou; Dong Wang

doi:10.1145/3487351.3488318

A Deep Contrastive Learning Approach to Extremely-Sparse Disaster Damage Assessment in Social Sensing

Yang Zhang, Ruohan Zong, Lanyu Shang, Ziyi Kou, Dong Wang

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	Proceedings Of The 2021 Ieee/acm International Conference On Advances In Social Networks Analysis And Mining, Asonam 2021
Editors	M Coscia, A Cuzzocrea, K Shu
Pages	151-158
Number of pages	8
DOIs	https://doi.org/10.1145/3487351.3488318
State	Published - 2021

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10.1145/3487351.3488318

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Zhang, Y., Zong, R., Shang, L., Kou, Z., & Wang, D. (2021). A Deep Contrastive Learning Approach to Extremely-Sparse Disaster Damage Assessment in Social Sensing. In M. Coscia, A. Cuzzocrea, & K. Shu (Eds.), Proceedings Of The 2021 Ieee/acm International Conference On Advances In Social Networks Analysis And Mining, Asonam 2021 (pp. 151-158) https://doi.org/10.1145/3487351.3488318

A Deep Contrastive Learning Approach to Extremely-Sparse Disaster Damage Assessment in Social Sensing. / Zhang, Yang; Zong, Ruohan; Shang, Lanyu et al.
Proceedings Of The 2021 Ieee/acm International Conference On Advances In Social Networks Analysis And Mining, Asonam 2021. ed. / M Coscia; A Cuzzocrea; K Shu. 2021. p. 151-158.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, Y, Zong, R, Shang, L, Kou, Z & Wang, D 2021, A Deep Contrastive Learning Approach to Extremely-Sparse Disaster Damage Assessment in Social Sensing. in M Coscia, A Cuzzocrea & K Shu (eds), Proceedings Of The 2021 Ieee/acm International Conference On Advances In Social Networks Analysis And Mining, Asonam 2021. pp. 151-158. https://doi.org/10.1145/3487351.3488318

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A Deep Contrastive Learning Approach to Extremely-Sparse Disaster Damage Assessment in Social Sensing

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