University of Twente
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Dr. habil. Y. Yang (Michael)

Universitair docent

Over mij

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/

Expertises

Engineering & Materials Science
# Convolutional Neural Networks
# Deep Learning
# Image Classification
# Object Detection
# Semantics
# Unmanned Aerial Vehicles (Uav)
Earth & Environmental Sciences
# Segmentation
Physics & Astronomy
# Semantics

Verbonden aan

Onderzoek

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.

Publicaties

Recent
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
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
Zhang, Z. (2022). Photogrammetric point clouds: quality assessment, filtering, and change detection. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036552653
Ding, J., Xue, N., Xia, G. S., Bai, X., Yang, W. , Yang, M., Belongie, S., Luo, J., Datcu, M., Pelillo, M., & Zhang, L. (2021). Object detection in aerial images: A large-scale benchmark and challenges. IEEE transactions on pattern analysis and machine intelligence. https://doi.org/10.1109/TPAMI.2021.3117983
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. 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., Landrieu, L., Tuia, D., & Toth, C. (2021). Muti-modal learning in photogrammetry and remote sensing. ISPRS journal of photogrammetry and remote sensing, 176, 54-54. https://doi.org/10.1016/j.isprsjprs.2021.03.022

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Naar mijn profiel

Onderwijs

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

Vakken Collegejaar  2021/2022

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
 

Vakken Collegejaar  2020/2021

Projecten

• 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

Contactgegevens

 
 

Bezoekadres

Universiteit Twente
Faculty of Geo-Information Science and Earth Observation
ITC (gebouwnr. 75), kamer 2-007
Hengelosestraat 99
7514AE  Enschede

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Postadres

Universiteit Twente
Faculty of Geo-Information Science and Earth Observation
ITC  2-007
Postbus 217
7500 AE Enschede

Verbonden aan

Overige contactinformatie

https://research.utwente.nl/en/persons/michael-ying-yang
Universiteit Twente Drienerlolaan 5
7522 NB Enschede
+31 (0)53 489 9111
info@utwente.nl
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