D2
Computer Vision and Machine Learning

Anna Kukleva (PhD Student)

MSc Anna Kukleva

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Standort
E1 4 - 615
Telefon
+49 681 9325 2144
Fax
+49 681 9325 2099

Personal Information

Publications

Duka, E., Kukleva, A., & Schiele, B. (2022). Leveraging Self-Supervised Training for Unintentional Action Recognition. Retrieved from https://arxiv.org/abs/2209.11870
(arXiv: 2209.11870)
Abstract
Unintentional actions are rare occurrences that are difficult to define<br>precisely and that are highly dependent on the temporal context of the action.<br>In this work, we explore such actions and seek to identify the points in videos<br>where the actions transition from intentional to unintentional. We propose a<br>multi-stage framework that exploits inherent biases such as motion speed,<br>motion direction, and order to recognize unintentional actions. To enhance<br>representations via self-supervised training for the task of unintentional<br>action recognition we propose temporal transformations, called Temporal<br>Transformations of Inherent Biases of Unintentional Actions (T2IBUA). The<br>multi-stage approach models the temporal information on both the level of<br>individual frames and full clips. These enhanced representations show strong<br>performance for unintentional action recognition tasks. We provide an extensive<br>ablation study of our framework and report results that significantly improve<br>over the state-of-the-art.<br>
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BibTeX
@online{Duka2209.11870, TITLE = {Leveraging Self-Supervised Training for Unintentional Action Recognition}, AUTHOR = {Duka, Enea and Kukleva, Anna and Schiele, Bernt}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2209.11870}, EPRINT = {2209.11870}, EPRINTTYPE = {arXiv}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Unintentional actions are rare occurrences that are difficult to define<br>precisely and that are highly dependent on the temporal context of the action.<br>In this work, we explore such actions and seek to identify the points in videos<br>where the actions transition from intentional to unintentional. We propose a<br>multi-stage framework that exploits inherent biases such as motion speed,<br>motion direction, and order to recognize unintentional actions. To enhance<br>representations via self-supervised training for the task of unintentional<br>action recognition we propose temporal transformations, called Temporal<br>Transformations of Inherent Biases of Unintentional Actions (T2IBUA). The<br>multi-stage approach models the temporal information on both the level of<br>individual frames and full clips. These enhanced representations show strong<br>performance for unintentional action recognition tasks. We provide an extensive<br>ablation study of our framework and report results that significantly improve<br>over the state-of-the-art.<br>}, }
Endnote
%0 Report %A Duka, Enea %A Kukleva, Anna %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Leveraging Self-Supervised Training for Unintentional Action Recognition : %G eng %U http://hdl.handle.net/21.11116/0000-000C-184F-2 %U https://arxiv.org/abs/2209.11870 %D 2022 %X Unintentional actions are rare occurrences that are difficult to define<br>precisely and that are highly dependent on the temporal context of the action.<br>In this work, we explore such actions and seek to identify the points in videos<br>where the actions transition from intentional to unintentional. We propose a<br>multi-stage framework that exploits inherent biases such as motion speed,<br>motion direction, and order to recognize unintentional actions. To enhance<br>representations via self-supervised training for the task of unintentional<br>action recognition we propose temporal transformations, called Temporal<br>Transformations of Inherent Biases of Unintentional Actions (T2IBUA). The<br>multi-stage approach models the temporal information on both the level of<br>individual frames and full clips. These enhanced representations show strong<br>performance for unintentional action recognition tasks. We provide an extensive<br>ablation study of our framework and report results that significantly improve<br>over the state-of-the-art.<br> %K Computer Science, Computer Vision and Pattern Recognition, cs.CV
VidalMata, R. G., Scheirer, W. J., Kukleva, A., Cox, D., & Kuehne, H. (2021). Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences. In IEEE Winter Conference on Applications of Computer Vision (WACV 2021). Virtual: Computer Vision Foundation.
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@inproceedings{VidalMata_WACV21, TITLE = {Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences}, AUTHOR = {VidalMata, Rosaura G. and Scheirer, Walter J. and Kukleva, Anna and Cox, David and Kuehne, Hilde}, LANGUAGE = {eng}, PUBLISHER = {Computer Vision Foundation}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE Winter Conference on Applications of Computer Vision (WACV 2021)}, PAGES = {1238--1247}, ADDRESS = {Virtual}, }
Endnote
%0 Conference Proceedings %A VidalMata, Rosaura G. %A Scheirer, Walter J. %A Kukleva, Anna %A Cox, David %A Kuehne, Hilde %+ External Organizations External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences : %G eng %U http://hdl.handle.net/21.11116/0000-0008-4593-4 %D 2021 %B IEEE Winter Conference on Applications of Computer Vision %Z date of event: 2021-01-05 - 2021-01-09 %C Virtual %B IEEE Winter Conference on Applications of Computer Vision %P 1238 - 1247 %I Computer Vision Foundation
Kukleva, A., Kuehne, H., & Schiele, B. (2021). Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting. In ICCV 2021, IEEE/CVF International Conference on Computer Vision. Virtual Event: IEEE. doi:10.1109/ICCV48922.2021.00889
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@inproceedings{Kukleva_ICCV21, TITLE = {Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting}, AUTHOR = {Kukleva, Anna and Kuehne, Hilde and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-1-6654-2812-5}, DOI = {10.1109/ICCV48922.2021.00889}, PUBLISHER = {IEEE}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {ICCV 2021, IEEE/CVF International Conference on Computer Vision}, PAGES = {9000--9009}, ADDRESS = {Virtual Event}, }
Endnote
%0 Conference Proceedings %A Kukleva, Anna %A Kuehne, Hilde %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting : %G eng %U http://hdl.handle.net/21.11116/0000-0009-810E-6 %R 10.1109/ICCV48922.2021.00889 %D 2021 %B IEEE/CVF International Conference on Computer Vision %Z date of event: 2021-10-11 - 2021-10-17 %C Virtual Event %B ICCV 2021 %P 9000 - 9009 %I IEEE %@ 978-1-6654-2812-5
Fan, Y., Kukleva, A., & Schiele, B. (2022). Revisiting Consistency Regularization for Semi-supervised Learning. In Pattern Recognition (GCPR 2021). Bonn, Germany: Springer. doi:10.1007/978-3-030-92659-5_5
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@inproceedings{Fan_GCPR2021, TITLE = {Revisiting Consistency Regularization for Semi-supervised Learning}, AUTHOR = {Fan, Yue and Kukleva, Anna and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-3-030-92659-5; 978-3-030-92658-8}, DOI = {10.1007/978-3-030-92659-5_5}, PUBLISHER = {Springer}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Pattern Recognition (GCPR 2021)}, EDITOR = {Bauckhage, Christian and Gall, J{\"u}rgen and Schwing, Alexander}, PAGES = {63--78}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {13024}, ADDRESS = {Bonn, Germany}, }
Endnote
%0 Conference Proceedings %A Fan, Yue %A Kukleva, Anna %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Revisiting Consistency Regularization for Semi-supervised Learning : %G eng %U http://hdl.handle.net/21.11116/0000-000C-4358-6 %R 10.1007/978-3-030-92659-5_5 %D 2022 %B 43rd German Conference on Pattern Recognition %Z date of event: 2021-09-28 - 2021-10-01 %C Bonn, Germany %B Pattern Recognition %E Bauckhage, Christian; Gall, J&#252;rgen; Schwing, Alexander %P 63 - 78 %I Springer %@ 978-3-030-92659-5 978-3-030-92658-8 %B Lecture Notes in Computer Science %N 13024
Fan, Y., Kukleva, A., Dai, D., & Schiele, B. (2023). Revisiting Consistency Regularization for Semi-supervised Learning. International Journal of Computer Vision, 131. doi:10.1007/s11263-022-01723-4
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@article{Fan22, TITLE = {Revisiting Consistency Regularization for Semi-supervised Learning}, AUTHOR = {Fan, Yue and Kukleva, Anna and Dai, Dengxin and Schiele, Bernt}, LANGUAGE = {eng}, ISSN = {0920-5691}, DOI = {10.1007/s11263-022-01723-4}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2023}, MARGINALMARK = {$\bullet$}, DATE = {2023}, JOURNAL = {International Journal of Computer Vision}, VOLUME = {131}, PAGES = {626--643}, }
Endnote
%0 Journal Article %A Fan, Yue %A Kukleva, Anna %A Dai, Dengxin %A Schiele, Bernt %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society %T Revisiting Consistency Regularization for Semi-supervised Learning : %G eng %U http://hdl.handle.net/21.11116/0000-000C-73A9-4 %R 10.1007/s11263-022-01723-4 %7 2022 %D 2023 %J International Journal of Computer Vision %O Int. J. Comput. Vis. %V 131 %& 626 %P 626 - 643 %I Springer %C New York, NY %@ false
Lin, W., Kukleva, A., Sun, K., Possegger, H., Kuehne, H., & Bischof, H. (2022). CycDA: Unsupervised Cycle Domain Adaptation to Learn from Image to Video. In Computer Vision -- ECCV 2022. Tel Aviv, Israel: Springer. doi:10.1007/978-3-031-20062-5_40
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@inproceedings{Lin_ECCV2022, TITLE = {{CycDA}: {U}nsupervised Cycle Domain Adaptation to Learn from Image to Video}, AUTHOR = {Lin, Wei and Kukleva, Anna and Sun, Kunyang and Possegger, Horst and Kuehne, Hilde and Bischof, Horst}, LANGUAGE = {eng}, ISBN = {978-3-031-20061-8}, DOI = {10.1007/978-3-031-20062-5_40}, PUBLISHER = {Springer}, YEAR = {2022}, MARGINALMARK = {$\bullet$}, DATE = {2022}, BOOKTITLE = {Computer Vision -- ECCV 2022}, EDITOR = {Avidan, Shai and Brostow, Gabriel and Ciss{\'e}, Moustapha and Farinella, Giovanni and Hassner, Tal}, PAGES = {698--715}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {13663}, ADDRESS = {Tel Aviv, Israel}, }
Endnote
%0 Conference Proceedings %A Lin, Wei %A Kukleva, Anna %A Sun, Kunyang %A Possegger, Horst %A Kuehne, Hilde %A Bischof, Horst %+ External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T CycDA: Unsupervised Cycle Domain Adaptation to Learn from Image to Video : %G eng %U http://hdl.handle.net/21.11116/0000-000C-7CDA-4 %R 10.1007/978-3-031-20062-5_40 %D 2022 %B 17th European Conference on Computer Vision %Z date of event: 2022-10-23 - 2022-10-27 %C Tel Aviv, Israel %B Computer Vision -- ECCV 2022 %E Avidan, Shai; Brostow, Gabriel; Ciss&#233;, Moustapha; Farinella, Giovanni; Hassner, Tal %P 698 - 715 %I Springer %@ 978-3-031-20061-8 %B Lecture Notes in Computer Science %N 13663 %U https://rdcu.be/c4q1A
Kukleva, A., Tapaswi, M., & Laptev, I. (2020). Learning Interactions and Relationships between Movie Characters. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). Seattle, WA, USA (Virtual): IEEE. doi:10.1109/CVPR42600.2020.00987
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@inproceedings{Kukleva_CVPR2020, TITLE = {Learning Interactions and Relationships between Movie Characters}, AUTHOR = {Kukleva, Anna and Tapaswi, Makarand and Laptev, Ivan}, LANGUAGE = {eng}, ISBN = {978-1-7281-7168-5}, DOI = {10.1109/CVPR42600.2020.00987}, PUBLISHER = {IEEE}, YEAR = {2020}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)}, PAGES = {9846--9855}, ADDRESS = {Seattle, WA, USA (Virtual)}, }
Endnote
%0 Conference Proceedings %A Kukleva, Anna %A Tapaswi, Makarand %A Laptev, Ivan %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Learning Interactions and Relationships between Movie Characters : %G eng %U http://hdl.handle.net/21.11116/0000-0008-458B-E %R 10.1109/CVPR42600.2020.00987 %D 2020 %B 33rd IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2020-06-14 - 2020-06-19 %C Seattle, WA, USA (Virtual) %B IEEE/CVF Conference on Computer Vision and Pattern Recognition %P 9846 - 9855 %I IEEE %@ 978-1-7281-7168-5