Search Results for author: Riccardo de Lutio

Found 4 papers, 3 papers with code

Learning Graph Regularisation for Guided Super-Resolution

1 code implementation CVPR 2022 Riccardo de Lutio, Alexander Becker, Stefano D'Aronco, Stefania Russo, Jan D. Wegner, Konrad Schindler

With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.

Super-Resolution

Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs

no code implementations7 Jun 2021 Riccardo de Lutio, Yihang She, Stefano D'Aronco, Stefania Russo, Philipp Brun, Jan D. Wegner, Konrad Schindler

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts.

Multimodal Deep Learning

The Herbarium 2021 Half-Earth Challenge Dataset

1 code implementation28 May 2021 Riccardo de Lutio, Damon Little, Barbara Ambrose, Serge Belongie

Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity.

Guided Super-Resolution as Pixel-to-Pixel Transformation

2 code implementations ICCV 2019 Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler

Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e. g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e. g., a grey-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map).

Super-Resolution

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