513 citations · 989 across the 12 of their papers we have counts for
7 papers · 1 filter
Photometric Redshift Estimation with a Convolutional Neural Network: NetZ
S. Schuldt, S. H. Suyu, R. Cañameras +4
The redshifts of galaxies are a key attribute that is needed for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, m…
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network. This approach is based on smooth…
HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
S. Schuldt, S. H. Suyu, T. Meinhardt +4
Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^…
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
Maxim Maximov, Kevin Galim, Laura Leal-Taixé
Data-driven depth estimation methods struggle with the generalization outside their training scenes due to the immense variability of the real-world scenes. This problem can be par…
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
Maxim Maximov, Ismail Elezi, Laura Leal-Taixé
The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like peop…
MOT20: A benchmark for multi object tracking in crowded scenes
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan +6
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
Learn to Predict Sets Using Feed-Forward Neural Networks
Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman +6
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a se…