Guided Depth Super-Resolution by Deep Anisotropic Diffusion

1 code implementation • 21 Nov 2022 • Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler

In this work, we propose a novel approach which combines guided anisotropic diffusion with a deep convolutional network and advances the state of the art for guided depth super-resolution.

Super-Resolution

Satellite-based high-resolution maps of cocoa for Côte d'Ivoire and Ghana

no code implementations • 13 Jun 2022 • Nikolai Kalischek, Nico Lang, Cécile Renier, Rodrigo Caye Daudt, Thomas Addoah, William Thompson, Wilma J. Blaser-Hart, Rachael Garrett, Konrad Schindler, Jan D. Wegner

C\^ote d'Ivoire and Ghana, the world's largest producers of cocoa, account for two thirds of the global cocoa production.

Zero-Shot Bird Species Recognition by Learning from Field Guides

1 code implementation • 3 Jun 2022 • Andrés C. Rodríguez, Stefano D'Aronco, Rodrigo Caye Daudt, Jan D. Wegner, Konrad Schindler

The illustrations contained in field guides deliberately focus on discriminative properties of a species, and can serve as side information to transfer knowledge from seen to unseen classes.

Generalized Zero-Shot Learning

FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

1 code implementation • 31 May 2022 • Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler

We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.

Multi-Task Learning Probabilistic Deep Learning

Weakly Supervised Change Detection Using Guided Anisotropic Difusion

no code implementations • 31 Dec 2021 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau

Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms.

Change Detection Semantic Segmentation +1

Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection

no code implementations • 17 Apr 2019 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau

Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms.

Change Detection Semantic Segmentation +1

Multitask Learning for Large-scale Semantic Change Detection

no code implementations • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau

In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection.

Change Detection

Fully Convolutional Siamese Networks for Change Detection

3 code implementations • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch

This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.

Change Detection Change detection for remote sensing images

Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks

1 code implementation • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau

The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate.

Change Detection