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
Controllable Privacy in Face Recognition: A Filter-based Approach (2024)In 2024 IEEE International Joint Conference on Biometrics (IJCB). Article 10744519. IEEE. Rezgui, Z., Strisciuglio, N. & Veldhuis, R.https://doi.org/10.1109/IJCB62174.2024.10744519Synthetic Data-Based Training of Instance Segmentation: A Robotic Bin-Picking Pipeline for Chicken Fillets (2024)In 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024 (pp. 2805-2812) (IEEE International Conference on Automation Science and Engineering). IEEE. Jonker, M., Roozing, W. & Strisciuglio, N.https://doi.org/10.1109/CASE59546.2024.10711568CAST: 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.2024.08.024Fourier-Basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification (2024)In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17763-17772). Article 10655510. IEEE. Vaish, P., Wang, S. & Strisciuglio, N.https://doi.org/10.1109/CVPR52733.2024.01682Quantifying 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 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.2312.05119 Research profiles
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