Dr. habil. Y. Yang (Michael)

Dr. Michael Ying Yang is currently Assistant Professor with University of Twente (the Netherlands), heading a group working on scene understanding.

He received the PhD degree (summa cum laude) from University of Bonn (Germany) in 2011. From 2008 to 2012, he worked as Researcher with the Department of Photogrammetry, University of Bonn. From 2012 to 2015, he was a Postdoctoral Researcher with the Institute for Information Processing, Leibniz University Hannover. From 2015 to 2016, he was a Senior Researcher at TU Dresden.

His research interests are in the fields of computer vision and photogrammetry with specialization on scene understanding and semantic interpretation from imagery. He published over 100 papers in international journals and conference proceedings and currently co-supervise 4 PhD students. He serves as Associate Editor of ISPRS Journal of Photogrammetry and Remote Sensing, co-chair of ISPRS working group II/5 Dynamic Scene Analysis, and recipient of the ISPRS President's Honorary Citation (2016) and Best Science Paper Award at BMVC 2016. Since 2016, he is a Senior Member of IEEE. He is regularly serving as program committee member of conferences and reviewer for international journals.

Please visit personal homepage for up-to-date details:

https://sites.google.com/site/michaelyingyang/

Engineering & Materials Science

# Convolutional Neural Networks
# Deep Learning
# Image Classification
# Object Detection
# Semantics
# Unmanned Aerial Vehicles (Uav)

Earth & Environmental Sciences

# Segmentation

Physics & Astronomy

# Semantics

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Earth Observation Science (EOS)

His research is in the fields of Artificial intelligence (AI), Computer Vision and Photogrammetry with specialization on Deep Learning, Graphical Models, Scene Understanding, and Multi-Sensor Fusion.

Wei, Y. , Vosselman, G. , & Yang, M. Y. (2022). Flow-based GAN for 3D point cloud generation from a single image. ArXiv. https://doi.org/10.48550/arXiv.2210.04072

Lin, Y. (2022). Deep learning for semantic segmentation of airborne laser scanning point clouds. [PhD Thesis - Research UT, graduation UT, Faculty of Geo-Information Science and Earth Observation, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036554107

Liao, W., Hu, K. , Yang, M. Y., & Rosenhahn, B. (2022). Text to Image Generation with Semantic-Spatial Aware GAN. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 18166-18175). IEEE. https://doi.org/10.1109/CVPR52688.2022.01765

Cao, Y., Luo, X., Yang, J., Cao, Y. , & Yang, M. Y. (2022). Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection. Information Fusion, 88, 1-11. https://doi.org/10.1016/j.inffus.2022.06.008

Fu, G., Jia, S., Zhu, W., Yang, J., Cao, Y. , Yang, M. Y., & Cao, Y. (2022). Fusion of multi-light source illuminated images for effective defect inspection on highly reflective surfaces. Mechanical systems and signal processing, 175, 1-13. [109109]. https://doi.org/10.1016/j.ymssp.2022.109109

Lin, Y. , Vosselman, G. , & Yang, M. Y. (2022). Weakly supervised semantic segmentation of airborne laser scanning point clouds. ISPRS journal of photogrammetry and remote sensing, 187, 79-100. https://doi.org/10.1016/j.isprsjprs.2022.03.001

He, S., Liao, W. , Yang, M. Y., Song, Y., Rosenhahn, B., & Xiang, T. (2022). Disentangled lifespan face synthesis. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 3857-3866). IEEE. https://doi.org/10.1109/ICCV48922.2021.00385

Cong, Y., Liao, W., Ackermann, H., Rosenhahn, B. , & Yang, M. Y. (2022). Spatial-temporal transformer for dynamic scene graph generation. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 16352-16362). IEEE. https://doi.org/10.1109/ICCV48922.2021.01606

Ding, J., Xue, N., Xia, G. S., Bai, X., Yang, W. , Yang, M., Belongie, S., Luo, J., Datcu, M., Pelillo, M., & Zhang, L. (2022). Object detection in aerial images: A large-scale benchmark and challenges. IEEE transactions on pattern analysis and machine intelligence, 44(11), 7778-7796. https://doi.org/10.1109/TPAMI.2021.3117983

Borsci, S. , Lehtola, V. V. , Nex, F. C. , Yang, M. Y. , Augustijn, P. W. M., Bagheriye, L. , Brune, C., Kounadi, O. , Li, J. J. , Rebelo Moreira, J. L. , van der Nagel, J. E. L. , Veldkamp, B. P. , Le, D. V. , Wang, M. , Wijnhoven, F. , Wolterink, J. M. , & Zurita-Milla, R. (2022). Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle. AI & society. https://doi.org/10.1007/s00146-021-01383-x, https://doi.org/10.1007/s00146-021-01383-x

Zhang, Z. (2022). Photogrammetric point clouds: quality assessment, filtering, and change detection. [PhD Thesis - Research UT, graduation UT, Faculty of Geo-Information Science and Earth Observation, University of Twente]. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036552653

Xia, G. S., Ding, J., Qian, M., Xue, N., Han, J., Bai, X. , Yang, M. Y., Li, S., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L., Zhou, Q., Yu, C. H., Hu, K., Bu, Y., Tan, W., Yang, Z., ... Liu, F. (2021). LUAI challenge 2021 on learning to understand aerial images. In Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 (pp. 762-768). (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2021-October). IEEE. https://doi.org/10.1109/ICCVW54120.2021.00090

Kluger, F., Ackermann, H., Brachmann, E. , Yang, M. Y., & Rosenhahn, B. (2021). Cuboids revisited: learning robust 3D shape fitting to single RGB images. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 13065-13074). IEEE. https://doi.org/10.1109/CVPR46437.2021.01287

He, S., Liao, W. , Yang, M. Y., Yang, Y., Song, Y., Rosenhahn, B., & Xiang, T. (2021). Context-aware layout to image generation with enhanced object appearance. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 15044-15053). IEEE. https://doi.org/10.1109/CVPR46437.2021.01480

Cheng, H., Liao, W., Tang, X. , Yang, M. Y., Sester, M., & Rosenhahn, B. (2021). Exploring dynamic context for multi-path trajectory prediction. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 12795-12801). IEEE. https://doi.org/10.1109/ICRA48506.2021.9562034

Kumaar, S. , Lyu, Y. , Nex, F. , & Yang, M. Y. (2021). CABiNet: Efficient Context Aggregation Network for Low-Latency Semantic Segmentation. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 13517-13524). (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2021-May). IEEE. https://doi.org/10.1109/ICRA48506.2021.9560977

Liao, W., Lan, C. , Yang, M. Y., Zeng, W., & Rosenhahn, B. (2021). Target-tailored source-transformation for scene graph generation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1663-1671). IEEE. https://doi.org/10.1109/CVPRW53098.2021.00182

Lyu, Y. (2021). Dynamic scene understanding using deep neural networks. [PhD Thesis - Research UT, graduation UT, Faculty of Geo-Information Science and Earth Observation, University of Twente]. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/0.3990/1.9789036552233

Lyu, Y. , Vosselman, G., Xia, G. S. , & Yang, M. Y. (2021). Bidirectional multi-scale attention networks for semantic segmentation of oblique UAV imagery. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, A. Yilmaz, J. D. Wegner, F. Remondino, T. Fuse, & I. Toschi (Eds.), ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission II (Vol. V-2-2021, pp. 75-82). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-annals-V-2-2021-75-2021

Yang, M. Y., Kumaar, S. , Lyu, Y. , & Nex, F. (2021). Real-time Semantic Segmentation with Context Aggregation Network. ISPRS journal of photogrammetry and remote sensing, 178, 124-134. https://doi.org/10.1016/j.isprsjprs.2021.06.006

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Main educational responsibilities are teaching topics on deep learning, scene understanding with UAVs, image processing, 3D modeling and photogrammetry.

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

• NWO eScience project 2021-2024

EcoExtreML:Accelerating Process Understanding for Ecosystem Functioning under Extreme Climates with Physics-Aware Machine Learning

Funded by The Netherlands eScience Center, co-PI

• DFG project 2017-2020-2023

Comprehensive Conjoint GPS and Video Data Analysis for Smart Maps

Funded by the German Research Foundation, co-PI

• DFG project 2015-2018

Holistic Scene Understanding

Funded by the German Research Foundation, PI

+31534892916

michael.yang@utwente.nl

Visiting Address

University of Twente
Faculty of Geo-Information Science and Earth Observation
ITC (building no. 75), room 2-007
Hengelosestraat 99
7514AE Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Geo-Information Science and Earth Observation
ITC 2-007
P.O. Box 217
7500 AE Enschede
The Netherlands

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Earth Observation Science (EOS)

https://research.utwente.nl/en/persons/michael-ying-yang

University of Twente Drienerlolaan 5
7522 NB Enschede

+31 (0)53 489 9111
info@utwente.nl
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