Margret Keuper (Research Leader)

Prof. Dr. Margret Keuper

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - 617
Phone
+49 681 9325 2117
Fax
+49 681 9325 2099

Publications

He, Y., Chiu, W.-C., Keuper, M., & Fritz, M. (2017). STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling. In 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Honolulu, HI, USA: IEEE Computer Society. doi:10.1109/CVPR.2017.757
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BibTeX
@inproceedings{yang_cvpr17, TITLE = {{STD2P}: {RGBD} Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling}, AUTHOR = {He, Yang and Chiu, Wei-Chen and Keuper, Margret and Fritz, Mario}, LANGUAGE = {eng}, ISBN = {978-1-5386-0458-8}, DOI = {10.1109/CVPR.2017.757}, PUBLISHER = {IEEE Computer Society}, YEAR = {2017}, DATE = {2017}, BOOKTITLE = {30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)}, PAGES = {7158--7167}, ADDRESS = {Honolulu, HI, USA}, }
Endnote
%0 Conference Proceedings %A He, Yang %A Chiu, Wei-Chen %A Keuper, Margret %A Fritz, Mario %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-B8E6-C %R 10.1109/CVPR.2017.757 %D 2017 %B 30th IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2017-07-22 - 2017-07-25 %C Honolulu, HI, USA %B 30th IEEE Conference on Computer Vision and Pattern Recognition %P 7158 - 7167 %I IEEE Computer Society %@ 978-1-5386-0458-8
He, Y., Chiu, W.-C., Keuper, M., & Fritz, M. (2016). RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling. Retrieved from http://arxiv.org/abs/1604.02388
(arXiv: 1604.02388)
Abstract
Beyond the success in classification, neural networks have recently shown strong results on pixel-wise prediction tasks like image semantic segmentation on RGBD data. However, the commonly used deconvolutional layers for upsampling intermediate representations to the full-resolution output still show different failure modes, like imprecise segmentation boundaries and label mistakes in particular on large, weakly textured objects (e.g. fridge, whiteboard, door). We attribute these errors in part to the rigid way, current network aggregate information, that can be either too local (missing context) or too global (inaccurate boundaries). Therefore we propose a data-driven pooling layer that integrates with fully convolutional architectures and utilizes boundary detection from RGBD image segmentation approaches. We extend our approach to leverage region-level correspondences across images with an additional temporal pooling stage. We evaluate our approach on the NYU-Depth-V2 dataset comprised of indoor RGBD video sequences and compare it to various state-of-the-art baselines. Besides a general improvement over the state-of-the-art, our approach shows particularly good results in terms of accuracy of the predicted boundaries and in segmenting previously problematic classes.
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BibTeX
@online{He_arXiv2016, TITLE = {{RGBD} Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling}, AUTHOR = {He, Yang and Chiu, Wei-Chen and Keuper, Margret and Fritz, Mario}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1604.02388}, EPRINT = {1604.02388}, EPRINTTYPE = {arXiv}, YEAR = {2016}, ABSTRACT = {Beyond the success in classification, neural networks have recently shown strong results on pixel-wise prediction tasks like image semantic segmentation on RGBD data. However, the commonly used deconvolutional layers for upsampling intermediate representations to the full-resolution output still show different failure modes, like imprecise segmentation boundaries and label mistakes in particular on large, weakly textured objects (e.g. fridge, whiteboard, door). We attribute these errors in part to the rigid way, current network aggregate information, that can be either too local (missing context) or too global (inaccurate boundaries). Therefore we propose a data-driven pooling layer that integrates with fully convolutional architectures and utilizes boundary detection from RGBD image segmentation approaches. We extend our approach to leverage region-level correspondences across images with an additional temporal pooling stage. We evaluate our approach on the NYU-Depth-V2 dataset comprised of indoor RGBD video sequences and compare it to various state-of-the-art baselines. Besides a general improvement over the state-of-the-art, our approach shows particularly good results in terms of accuracy of the predicted boundaries and in segmenting previously problematic classes.}, }
Endnote
%0 Report %A He, Yang %A Chiu, Wei-Chen %A Keuper, Margret %A Fritz, Mario %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-063C-5 %U http://arxiv.org/abs/1604.02388 %D 2016 %X Beyond the success in classification, neural networks have recently shown strong results on pixel-wise prediction tasks like image semantic segmentation on RGBD data. However, the commonly used deconvolutional layers for upsampling intermediate representations to the full-resolution output still show different failure modes, like imprecise segmentation boundaries and label mistakes in particular on large, weakly textured objects (e.g. fridge, whiteboard, door). We attribute these errors in part to the rigid way, current network aggregate information, that can be either too local (missing context) or too global (inaccurate boundaries). Therefore we propose a data-driven pooling layer that integrates with fully convolutional architectures and utilizes boundary detection from RGBD image segmentation approaches. We extend our approach to leverage region-level correspondences across images with an additional temporal pooling stage. We evaluate our approach on the NYU-Depth-V2 dataset comprised of indoor RGBD video sequences and compare it to various state-of-the-art baselines. Besides a general improvement over the state-of-the-art, our approach shows particularly good results in terms of accuracy of the predicted boundaries and in segmenting previously problematic classes. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV
Keuper, M., Andres, B., & Brox, T. (2015). Motion Trajectory Segmentation via Minimum Cost Multicuts. In ICCV 2015, IEEE International Conference on Computer Vision. Santiago, Chile: IEEE. doi:10.1109/ICCV.2015.374
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@inproceedings{keuper-2015b, TITLE = {Motion Trajectory Segmentation via Minimum Cost Multicuts}, AUTHOR = {Keuper, Margret and Andres, Bjoern and Brox, Thomas}, LANGUAGE = {eng}, ISBN = {1-4673-8390-5}, DOI = {10.1109/ICCV.2015.374}, PUBLISHER = {IEEE}, YEAR = {2015}, DATE = {2015}, BOOKTITLE = {ICCV 2015, IEEE International Conference on Computer Vision}, PAGES = {3271--3279}, ADDRESS = {Santiago, Chile}, }
Endnote
%0 Conference Proceedings %A Keuper, Margret %A Andres, Bjoern %A Brox, Thomas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations %T Motion Trajectory Segmentation via Minimum Cost Multicuts : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-DC42-F %R 10.1109/ICCV.2015.374 %D 2015 %B IEEE International Conference on Computer Vision %Z date of event: 2015-12-13 - 2015-12-16 %C Santiago, Chile %B ICCV 2015 %P 3271 - 3279 %I IEEE %@ 1-4673-8390-5
He, Y., Keuper, M., Schiele, B., & Fritz, M. (2017). Learning Dilation Factors for Semantic Segmentation of Street Scenes. In Pattern Recognition (GCPR 2017). Basel, Switzerland: Springer. doi:10.1007/978-3-319-66709-6_4
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@inproceedings{he17gcpr, TITLE = {Learning Dilation Factors for Semantic Segmentation of Street Scenes}, AUTHOR = {He, Yang and Keuper, Margret and Schiele, Bernt and Fritz, Mario}, LANGUAGE = {eng}, ISBN = {978-3-319-66708-9}, DOI = {10.1007/978-3-319-66709-6_4}, PUBLISHER = {Springer}, YEAR = {2017}, DATE = {2017}, BOOKTITLE = {Pattern Recognition (GCPR 2017)}, EDITOR = {Roth, Volker and Vetter, Thomas}, PAGES = {41--51}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10496}, ADDRESS = {Basel, Switzerland}, }
Endnote
%0 Conference Proceedings %A He, Yang %A Keuper, Margret %A Schiele, Bernt %A Fritz, Mario %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Learning Dilation Factors for Semantic Segmentation of Street Scenes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-B8F6-8 %R 10.1007/978-3-319-66709-6_4 %D 2017 %B 39th German Conference on Pattern Recognition %Z date of event: 2017-09-13 - 2017-09-15 %C Basel, Switzerland %B Pattern Recognition %E Roth, Volker; Vetter, Thomas %P 41 - 51 %I Springer %@ 978-3-319-66708-9 %B Lecture Notes in Computer Science %N 10496
Keuper, M., Levinkov, E., Bonneel, N., Layoue, G., Brox, T., & Andres, B. (2015). Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts. In ICCV 2015, IEEE International Conference on Computer Vision. Santiago, Chile: IEEE. doi:10.1109/ICCV.2015.204
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@inproceedings{keuper-2015a, TITLE = {Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts}, AUTHOR = {Keuper, Margret and Levinkov, Evgeny and Bonneel, Nicolas and Layoue, Guilaume and Brox, Thomas and Andres, Bjoern}, LANGUAGE = {eng}, ISBN = {978-1-4673-8390-5}, DOI = {10.1109/ICCV.2015.204}, PUBLISHER = {IEEE}, YEAR = {2015}, DATE = {2015}, BOOKTITLE = {ICCV 2015, IEEE International Conference on Computer Vision}, PAGES = {1751--1759}, ADDRESS = {Santiago, Chile}, }
Endnote
%0 Conference Proceedings %A Keuper, Margret %A Levinkov, Evgeny %A Bonneel, Nicolas %A Layoue, Guilaume %A Brox, Thomas %A Andres, Bjoern %+ External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-DC59-C %R 10.1109/ICCV.2015.204 %D 2015 %B IEEE International Conference on Computer Vision %Z date of event: 2015-12-13 - 2015-12-16 %C Santiago, Chile %B ICCV 2015 %P 1751 - 1759 %I IEEE %@ 978-1-4673-8390-5
Jung, S., & Keuper, M. (2021). Spectral Distribution Aware Image Generation. In Thirty-Fifth AAAI Conference on Artificial Intelligence. Virtual Conference: AAAI.
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@inproceedings{Jung_AAAI21, TITLE = {Spectral Distribution Aware Image Generation}, AUTHOR = {Jung, Steffen and Keuper, Margret}, LANGUAGE = {eng}, PUBLISHER = {AAAI}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Thirty-Fifth AAAI Conference on Artificial Intelligence}, ADDRESS = {Virtual Conference}, }
Endnote
%0 Conference Proceedings %A Jung, Steffen %A Keuper, Margret %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations %T Spectral Distribution Aware Image Generation : %G eng %U http://hdl.handle.net/21.11116/0000-0007-A808-3 %D 2021 %B Thirty-Fifth AAAI Conference on Artificial Intelligence %Z date of event: 2021-02-02 - 2021-02-09 %C Virtual Conference %B Thirty-Fifth AAAI Conference on Artificial Intelligence %I AAAI