Search Results for author: Piotr Bilinski

Found 9 papers, 3 papers with code

ML framework for global river flood predictions based on the Caravan dataset

no code implementations14 Nov 2022 Ioanna Bouri, Manu Lahariya, Omer Nivron, Enrique Portales Julia, Dietmar Backes, Piotr Bilinski, Guy Schumann

Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset.

Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows

no code implementations10 Nov 2022 Abdelhamid Ezzerg, Thomas Merritt, Kayoko Yanagisawa, Piotr Bilinski, Magdalena Proszewska, Kamil Pokora, Renard Korzeniowski, Roberto Barra-Chicote, Daniel Korzekwa

Regional accents of the same language affect not only how words are pronounced (i. e., phonetic content), but also impact prosodic aspects of speech such as speaking rate and intonation.

G3AN: Disentangling Appearance and Motion for Video Generation

1 code implementation CVPR 2020 Yaohui Wang, Piotr Bilinski, Francois Bremond, Antitza Dantcheva

Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion.

Video Generation

Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

1 code implementation3 Jan 2019 Bradley Gram-Hansen, Patrick Helber, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, Piotr Bilinski

2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs.

BIG-bench Machine Learning

Multi$^{\mathbf{3}}$Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

1 code implementation5 Dec 2018 Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.

Flooded Building Segmentation

Generating Material Maps to Map Informal Settlements

no code implementations30 Nov 2018 Patrick Helber, Bradley Gram-Hansen, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, Piotr Bilinski

Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals.

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