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
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)
Peter van der Wal (University of Groningen, Information systems group)

Completed PhD supervision
Dr. Maria Leyva Vallina (University of Groningen, The Netherlands - 2023)
Dr. Virginia Riego del Castillo (University of Leon, Spain - 2022)
Dr. Vincenzo Vigilante (University of Salerno, Italy - 2021)

Publications

Jump to: 2024 | 2023

2024

CAST: Clustering self-Attention using Surrogate Tokens for efficient transformers (2024)Pattern recognition letters, 186, 30-36. van Engelenhoven, A., Strisciuglio, N. & Talavera, E.https://doi.org/10.1016/j.patrec.2024.08.024Quantifying White Matter Hyperintensity and Brain Volumes in Heterogeneous Clinical and Low-Field Portable MRI (2024)In 2024 IEEE International Symposium on Biomedical Imaging (ISBI): 27-30 May, 2024 - Athens, Greece, Megaron Athens International Conference Centre (pp. 1-5). Article 10635502. IEEE. Laso, P., Cerri, S., Sorby-Adams, A., Guo, J., Mateen, F., Goebl, P., Wu, J., Liu, P., Li, H. B., Young, S. I., Billot, B., Puonti, O., Sze, G., Payabavash, S., DeHavenon, A., Sheth, K. N., Rosen, M. S., Kirsch, J., Strisciuglio, N., … Iglesias, J. E.https://doi.org/10.1109/ISBI56570.2024.10635502Optimized network for detecting burr-breakage in images of milling workpieces (2024)Logic Journal of the IGPL, 32(4), 624-633. Del Castillo, V. R., Sánchez-González, L. & Strisciuglio, N.https://doi.org/10.1093/jigpal/jzae024Gender Privacy Angular Constraints for Face Recognition (2024)IEEE Transactions on Biometrics, Behavior, and Identity Science, 6(3), 352-363. Rezgui, Z., Strisciuglio, N. & Veldhuis, R.https://doi.org/10.1109/TBIOM.2024.3390586Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification: ImageNet models (2024)[Dataset Types › Dataset]. Zenodo. Vaish, P., Wang, S. & Strisciuglio, N.https://doi.org/10.5281/zenodo.13755776Rda-Inr: Riemannian Diffeomorphic Autoencoding Via Implicit Neural Representations (2024)[Contribution to conference › Poster] SIAM Conference on Imaging Science, IS 2024. Dummer, S., Strisciuglio, N. & Brune, C.Enhancing Soft Biometric Face Template Privacy with Mutual Information-Based Image Attacks (2024)In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (pp. 1141-1149). Article 10495713. IEEE. Rezgui, Z., Strisciuglio, N. & Veldhuis, R.https://doi.org/10.1109/WACVW60836.2024.00124Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification (2024)[Working paper › Preprint]. ArXiv.org. Vaish, P., Wang, S. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2403.01944Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification: CIFAR-10 models (2024)[Dataset Types › Dataset]. Zenodo. Vaish, P., Wang, S. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2403.01944Regressing Transformers for Data-efficient Visual Place Recognition (2024)[Working paper › Preprint]. ArXiv.org. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.Regressing Transformers for Data-efficient Visual Place Recognition (2024)In 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 (pp. 15898-15904). IEEE. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.https://doi.org/10.1109/ICRA57147.2024.10611288

2023

Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI (2023)[Working paper › Preprint]. ArXiv.org. Laso, P., Cerri, S., Sorby-Adams, A., Guo, J., Mateen, F., Goebl, P., Wu, J., Liu, P., Li, H., Young, S. I., Billot, B., Puonti, O., Sze, G., Payabavash, S., DeHavenon, A., Sheth, K. N., Rosen, M. S., Kirsch, J., Strisciuglio, N., … Iglesias, J. E.https://doi.org/10.48550/arXiv.2312.05119DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning (2023)In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (pp. 129-138). Article 10350684 (Proceedings IEEE/CVF International Conference on Computer Vision Workshops (ICCVW); Vol. 2023). IEEE. Wang, S., Brune, C., Veldhuis, R. & Strisciuglio, N.https://doi.org/10.1109/ICCVW60793.2023.00020What do neural networks learn in image classification? A frequency shortcut perspective (2023)In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1433-1442). Article 10378114 (Proceedings IEEE/CVF International Conference on Computer Vision (ICCV); Vol. 2023). IEEE. Wang, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.1109/ICCV51070.2023.00138Riemannian Shape Manifold Learning with Applications to Biological Data (2023)[Contribution to conference › Poster] EEMCS Research Networking Day 2023. Dummer, S., Strisciuglio, N. & Brune, C.

Research profiles

Affiliated study programs

Courses academic year 2024/2025

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 2023/2024

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University of Twente

Zilverling (building no. 11), room 4122
Hallenweg 19
7522 NH Enschede
Netherlands

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