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ORCIDStefan Oehmcke
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2020 – today
- 2024
[c23]Vishal Nedungadi, Ankit Kariryaa
, Stefan Oehmcke, Serge J. Belongie, Christian Igel, Nico Lang:
MMEarth: Exploring Multi-modal Pretext Tasks for Geospatial Representation Learning. ECCV (64) 2024: 164-182- [i12]Vishal Nedungadi, Ankit Kariryaa, Stefan Oehmcke, Serge J. Belongie, Christian Igel, Nico Lang:
MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning. CoRR abs/2405.02771 (2024) - [i11]Venkanna Babu Guthula, Stefan Oehmcke, Remigio Chilaule, Hui Zhang, Nico Lang, Ankit Kariryaa, Johan Mottelson, Christian Igel:
Nacala-Roof-Material: Drone Imagery for Roof Detection, Classification, and Segmentation to Support Mosquito-borne Disease Risk Assessment. CoRR abs/2406.04949 (2024) - 2023
- [j5]Changsheng Zhou, Chao Yuan, Hongxin Wang, Lei Li, Stefan Oehmcke, Junmin Liu, Jigen Peng:
Multi-scale pseudo labeling for unsupervised deep edge detection. Knowl. Based Syst. 280: 111057 (2023) - [j4]Christian Igel, Stefan Oehmcke:
Remember to Correct the Bias When Using Deep Learning for Regression! Künstliche Intell. 37(1): 33-40 (2023) - [c22]Dmitry Serykh, Stefan Oehmcke, Cosmin E. Oancea, Dainius Masiliunas, Jan Verbesselt, Yan Cheng, Stéphanie Horion, Fabian Gieseke, Nikolaj Hinnerskov:
Seasonal-Trend Time Series Decomposition on Graphics Processing Units. IEEE Big Data 2023: 5914-5923 - [c21]Hui Zhang, Ankit Kariryaa, Venkanna Babu Guthula, Christian Igel, Stefan Oehmcke:
Predicting urban tree cover from incomplete point labels and limited background information. UrbanAI@SIGSPATIAL 2023: 52-60 - [i10]Lei Li, Tianfang Zhang, Stefan Oehmcke, Fabian Gieseke, Christian Igel:
BuildSeg: A General Framework for the Segmentation of Buildings. CoRR abs/2301.06190 (2023) - [i9]Hui Zhang, Ankit Kariryaa, Venkanna Babu Guthula, Christian Igel, Stefan Oehmcke:
Predicting urban tree cover from incomplete point labels and limited background information. CoRR abs/2311.11592 (2023) - 2022
- [j3]Jaime C. Revenga, Katerina Trepekli, Stefan Oehmcke, Rasmus Jensen, Lei Li, Christian Igel, Fabian Gieseke, Thomas Friborg:
Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV-LiDAR and Machine Learning Methods. Remote. Sens. 14(16): 3912 (2022) - [c20]Stefan Oehmcke, Lei Li, Jaime C. Revenga, Thomas Nord-Larsen, Katerina Trepekli, Fabian Gieseke, Christian Igel:
Deep learning based 3D point cloud regression for estimating forest biomass. SIGSPATIAL/GIS 2022: 38:1-38:4 - [c19]Stefan Oehmcke, Fabian Gieseke:
Input Selection for Bandwidth-Limited Neural Network Inference. SDM 2022: 280-288 - [i8]Christian Igel, Stefan Oehmcke:
Remember to correct the bias when using deep learning for regression! CoRR abs/2203.16470 (2022) - [i7]Tianfang Zhang, Lei Li, Christian Igel, Stefan Oehmcke, Fabian Gieseke, Zhenming Peng:
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing. CoRR abs/2212.09088 (2022) - 2021
- [j2]Bent Sortkær, Emil Smith, David Reimer, Stefan Oehmcke, Ida Gran Andersen:
Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app. EPJ Data Sci. 10(1): 44 (2021) - [c18]Stefan Oehmcke, Thomas Nyegaard-Signori, Kenneth Grogan, Fabian Gieseke:
Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning. IEEE BigData 2021: 4915-4924 - [c17]Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard:
Attentional Feature Fusion. WACV 2021: 3559-3568 - [i6]Stefan Oehmcke, Lei Li, Jaime C. Revenga, Thomas Nord-Larsen, Katerina Trepekli, Fabian Gieseke, Christian Igel:
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass. CoRR abs/2112.11335 (2021) - 2020
- [c16]Stefan Oehmcke, Tzu-Hsin Karen Chen, Alexander V. Prishchepov, Fabian Gieseke:
Creating cloud-free satellite imagery from image time series with deep learning. BigSpatial@SIGSPATIAL 2020: 3:1-3:10 - [c15]Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard:
Attention as Activation. ICPR 2020: 9156-9163 - [c14]Stefan Oehmcke, Thomas Teusch, Thorben Petersen, Thorsten Klüner, Oliver Kramer:
Modeling H2O/Rutile-TiO2(110) Potential Energy Surfaces with Deep Networks. IJCNN 2020: 1-7 - [i5]Yimian Dai, Stefan Oehmcke, Yiquan Wu, Kobus Barnard:
Attention as Activation. CoRR abs/2007.07729 (2020) - [i4]Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard:
Attentional Feature Fusion. CoRR abs/2009.14082 (2020)
2010 – 2019
- 2019
- [c13]Vinnie Ko, Stefan Oehmcke, Fabian Gieseke:
Magnitude and Uncertainty Pruning Criterion for Neural Networks. IEEE BigData 2019: 2317-2326 - [c12]Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles, Martin Brandt, Fabian Gieseke:
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. IEEE BigData 2019: 2403-2412 - [c11]Tim Silhan, Stefan Oehmcke, Oliver Kramer:
Evolution of Stacked Autoencoders. CEC 2019: 823-830 - [i3]Stefan Oehmcke, Fabian Gieseke:
Learning Selection Masks for Deep Neural Networks. CoRR abs/1906.04673 (2019) - [i2]Vinnie Ko, Stefan Oehmcke, Fabian Gieseke:
Magnitude and Uncertainty Pruning Criterion for Neural Networks. CoRR abs/1912.04845 (2019) - [i1]Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles, Martin Brandt, Fabian Gieseke:
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. CoRR abs/1912.05026 (2019) - 2018
- [b1]Stefan Oehmcke:
Deep learning of virtual marine sensors. University of Oldenburg, Germany, 2018 - [j1]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Input quality aware convolutional LSTM networks for virtual marine sensors. Neurocomputing 275: 2603-2615 (2018) - [c10]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Direct Training of Dynamic Observation Noise with UMarineNet. ICANN (1) 2018: 123-133 - [c9]Stefan Oehmcke, Oliver Kramer:
Knowledge Sharing for Population Based Neural Network Training. KI 2018: 258-269 - 2017
- [c8]Manish Aggarwal, Justin Heinermann, Stefan Oehmcke, Oliver Kramer:
Preferences-Based Choice Prediction in Evolutionary Multi-objective Optimization. EvoApplications (1) 2017: 715-724 - [c7]Wei Lee Woon, Stefan Oehmcke, Oliver Kramer:
Spatio-Temporal Wind Power Prediction Using Recurrent Neural Networks. ICONIP (5) 2017: 556-563 - [c6]Daniel Lückehe, Stefan Oehmcke, Oliver Kramer:
Manifold learning with iterative dimensionality photo-projection. IJCNN 2017: 2555-2561 - [c5]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Recurrent neural networks and exponential PAA for virtual marine sensors. IJCNN 2017: 4459-4466 - 2016
- [c4]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
kNN ensembles with penalized DTW for multivariate time series imputation. IJCNN 2016: 2774-2781 - 2015
- [c3]Stefan Oehmcke, Justin Heinermann, Oliver Kramer:
Analysis of Diversity Methods for Evolutionary Multi-objective Ensemble Classifiers. EvoApplications 2015: 567-578 - [c2]Stefan Oehmcke, Oliver Zielinski, Oliver Kramer:
Event Detection in Marine Time Series Data. KI 2015: 279-286 - 2013
- [c1]Benjamin Poppinga, Stefan Oehmcke, Wilko Heuten, Susanne Boll:
Storyteller: in-situ reflection on study experiences. Mobile HCI 2013: 472-475
Coauthor Index
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