most citedNIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2021★ 2 cited

NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks

Jie Wei, Zhigang Zhu, Erik Blasch +5

During natural disasters, aircraft and satellites are used to survey the impacted regions. Usually human experts are needed to manually label the degrees of the building damage so…

cs.CG2021

ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results

Nina Miolane, Matteo Caorsi, Umberto Lupo +30

This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…