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dr. N. Strisciuglio (Nicola)

Assistant Professor

Research

My research in Computer Vision and Machine Learning revolves around the problems of generalization and robustness to changing environments and perturbations of the input data. I focus on identifying and mitigating the sources of bias in training data, while inducing good bias via expert and prior knowledge in the training process, model architectures or labels.

I address the understanding of the training data characteristics and the learning dynamics of computer vision models, to identify sources of bias and counteract them. I investigate the use of prior knowledge, in the form of improved labeling procedures to better exploit data semantics, in the design of novel architectural elements or to steer the learning process (e.g. via physics-informed models) for the training of data-efficient models, taking into account aspects related to training time and energy consumption. I envision efficient and robust computer vision models with embedded prior knowledge that are able to perform unbiased predictions, exploiting the semantics of the data rather than shortcut solutions, with reduced training time, size of the parameter space and energy  consumption, with comparable performance to larger models trained with massive datasets and with higher computational requirements.

Ongoing PhD supervision
Maria Leyva Vallina (University of Groningen, Intelligent Systems group)
Shunxin Wang (University of Twente, Data Management and Biometrics group)
Zohra Rezgui (University of Twente, Data Management and Biometrics group)
Sven Dummer (University of Twente, Mathematics of Imaging and AI group)
Melissa Tijink (University of Twente, Data Management and Biometrics group)

Completed PhD supervision
Dr. Vincenzo Vigilante (University of Salerno, Italy - 2020)
Dr. Virginia Riego del Castillo (University of Leon, Spain - 2022)

Publications

Recent
Leyva-Vallina, M. , Strisciuglio, N., & Petkov, N. (2023). Data-efficient visual place recognition with graded similarity supervision. Paper presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, British Columbia, Canada. https://openaccess.thecvf.com/content/CVPR2023/papers/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.pdf
Rivera-Arbeláez, J. M., Keekstra, D., Cofiño-Fabres, C., Boonen, T., Dostanic, M., ten Den, S. A., Vermeul, K., Mastrangeli, M. , van den Berg, A. , Segerink, L. I., Ribeiro, M. C. , Strisciuglio, N. , & Passier, R. (2023). Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking. Bioengineering and Translational Medicine, 8(3), [e10513]. https://doi.org/10.1002/btm2.10513
Greco, A. , Strisciuglio, N., Vento, M., & Vigilante, V. (2023). Benchmarking deep networks for facial emotion recognition in the wild. Multimedia tools and applications, 82(8), 11189-11220. https://doi.org/10.1007/s11042-022-12790-7
Wang, S. , Veldhuis, R. , Brune, C. , & Strisciuglio, N. (2022). Frequency Shortcut Learning in Neural Networks. In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications https://openreview.net/forum?id=zAfUHtSGWw
Strisciuglio, N., & Azzopardi, G. (2022). Visual response inhibition for increased robustness of convolutional networks to distribution shifts. Paper presented at 36th Annual Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, Louisiana, United States. https://openreview.net/forum?id=enByqfq18t
Dummer, S. , Strisciuglio, N. , & Brune, C. (2022). Structure preserving implicit shape encoding via flow regularization. Abstract from Geometric Deep Learning in Medical Image Analysis, Amsterdam, Netherlands. https://openreview.net/pdf?id=YcjlgyX_Ur1
Riego Del Castillo, V., Sánchez-González, L., Campazas-Vega, A. , & Strisciuglio, N. (2022). Vision-Based Module for Herding with a Sheepdog Robot. Sensors (Basel, Switzerland), 22(14), [5321]. https://doi.org/10.3390/s22145321
Pandey, V. , Brune, C. , & Strisciuglio, N. (2022). Self-supervised Learning Through Colorization for Microscopy Images. In S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, & F. Tombari (Eds.), Image Analysis and Processing – ICIAP 2022: 21st International Conference, Lecce, Italy, May 23-27, 2022. Proceedings, Part II (pp. 621-632). (Lecture Notes in Computer Science; Vol. 13232). Springer. https://doi.org/10.1007/978-3-031-06430-2_52
Brandt, R. , Strisciuglio, N., & Petkov, N. (2021). MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching. In N. Tsapatsoulis, A. Panayides, T. Theocharides, A. Lanitis, A. Lanitis, C. Pattichis, C. Pattichis, & M. Vento (Eds.), Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings (pp. 110-119). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13052 LNCS). Springer. https://doi.org/10.1007/978-3-030-89128-2_11
Strisciuglio, N., & Petkov, N. (2021). Brain-Inspired Algorithms for Processing of Visual Data. In K. Amunts, L. Grandinetti, T. Lippert, & N. Petkov (Eds.), Brain-Inspired Computing - 4th International Workshop, BrainComp 2019, Revised Selected Papers (pp. 105-115). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12339 LNCS). Springer. https://doi.org/10.1007/978-3-030-82427-3_8
Riego, V., Sánchez-González, L., Fernández-Robles, L., Gutiérrez-Fernández, A. , & Strisciuglio, N. (2021). Burr detection and classification using RUSTICO and image processing. Journal of computational science, 56, [101485]. https://doi.org/10.1016/j.jocs.2021.101485
Mehra, A. , Spreeuwers, L. , & Strisciuglio, N. (2021). Deepfake detection using capsule networks and long short-term memory networks. In G. M. Farinella, P. Radeva, J. Braz, & K. Bouatouch (Eds.), Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP (pp. 407-414). SCITEPRESS. https://doi.org/10.5220/0010289004070414

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Bachelor

Master

Courses Academic Year  2023/2024

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.
 

Courses Academic Year  2022/2023

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Visiting Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 4122
Hallenweg 19
7522NH  Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  4122
P.O. Box 217
7500 AE Enschede
The Netherlands

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University of Twente Drienerlolaan 5
7522 NB Enschede
+31 (0)53 489 9111
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