513 citations · 989 across the 12 of their papers we have counts for
12 papers · 1 filter
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
Lukas Kondmann, Aysim Toker, Sudipan Saha +3
Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Cha…
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
Aysim Toker, Qunjie Zhou, Maxim Maximov +1
The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. Th…
Learning Intra-Batch Connections for Deep Metric Learning
Jenny Seidenschwarz, Ismail Elezi, Laura Leal-Taixé
The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric l…
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…
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…
Learning a Neural Solver for Multiple Object Tracking
Guillem Brasó, Laura Leal-Taixé
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning method…
HistoNet: Predicting size histograms of object instances
Kishan Sharma, Moritz Gold, Christian Zurbruegg +2
We propose to predict histograms of object sizes in crowded scenes directly without any explicit object instance segmentation. What makes this task challenging is the high density…
Fusion of Head and Full-Body Detectors for Multi-Object Tracking
Roberto Henschel, Laura Leal-Taixé, Daniel Cremers +1
In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach. Yet, relying solely on a single detector is also a major li…
Deep Depth From Focus
Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab +2
Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhib…