M.Sc. Simon Reiß

About me

Hello there, my name is Simon, I work as a research assistant here at the Computer Vision for Human Computer Interaction Lab.

I am excited by democratizising machine learning and making it's benefits accessible to endeavours of all sizes.
In my research, I aim at breaking down the obstacles when deploying vision technology by designing cost-efficient, economical methods while upholding excellent performance.
I develop computer vision algorithms that leverage small and inexpensive data to solve semantic segmentation tasks.

If you as a passionate student are interested in bringing streamlined semantic segmentation systems to all developers regardless of how niche the field of application, shoot me an e-mail and come work with me (see below for open thesis topics).

Topics that interest me most include but are not limited to, semi-supervised learning, weakly-supervised learning, self-supervised learning, medical image segmentation as well as unsupervised image segmentation.

I work in a close collaboration with the Carl Zeiss AG.

 

 

Publications & Collaborations


Capturing omni-range context for omnidirectional segmentation
Yang, K.; Zhang, J.; Reiß, S.; Hu, X.; Stiefelhagen, R.
2021. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1376–1386, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CVPR46437.2021.00143
Graph-Constrained Contrastive Regularization for Semi-weakly Volumetric Segmentation
Reiß, S.; Seibold, C.; Freytag, A.; Rodner, E.; Stiefelhagen, R.
2022. Computer Vision – ECCV 2022 – 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXI. Ed.: S. Avidan, 401–419, Springer Nature Switzerland AG. doi:10.1007/978-3-031-19803-8_24
Breaking with Fixed Set Pathology Recognition Through Report-Guided Contrastive Training
Seibold, C.; Reiß, S.; Sarfraz, M. S.; Stiefelhagen, R.; Kleesiek, J.
2022. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 – 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V. Ed.: L. Wang, 690–700, Springer International Publishing. doi:10.1007/978-3-031-16443-9_66
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
Seibold, C.; Reiß, S.; Sarfraz, M. S.; Stiefelhagen, R.; Kleesiek, J.
2022. doi:10.5445/IR/1000146800
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation
Seibold, C.; Reiß, S.; Kleesiek, J.; Stiefelhagen, R.
2022. Thirty-sixth AAAI conference on artificial intelligence. Online, 22.02.2022 - 01.03.2022, 2171–2179, Association for the Advancement of Artificial Intelligence (AAAI)
Deep Classification-driven Domain Adaptation for Cross-Modal Driver Behavior Recognition
Reiß, S.; Roitberg, A.; Haurilet, M.; Stiefelhagen, R.
2020. 31st IEEE Intelligent Vehicles Symposium, IV 2020, Virtual, Las Vegas, United States, 19 October 2020 through 13 November 2020, 1042–1047, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IV47402.2020.9304782
Activity-aware attributes for zero-shot driver behavior recognition
Reiß, S.; Roitberg, A.; Haurilet, M.; Stiefelhagen, R.
2020. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020; Virtual, Online; United States; 14 June 2020 through 19 June 2020, 3950–3955, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CVPRW50498.2020.00459
Drive&Act: A Multi-modal Dataset for Fine-grained Driver Behavior Recognition in Autonomous Vehicles
Martin, M.; Roitberg, A.; Haurilet, M.; Horne, M.; Reiß, S.; Voit, M.; Stiefelhagen, R.
2019. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 27 Oct.-2 Nov. 2019, 2801–2810, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICCV.2019.00289

Currently no open thesis topics

  • Type:Master Thesis

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