N. Pantelidis, D. Georgalis, M. Pegia, D. Galanopoulos, K. Apostolidis, K. Stavrothanasopoulos, A. Moumtzidou, K. Gkountakos, I. Gialampoukidis, S. Vrochidis, V. Mezaris, Ι. Kompatsiaris, “VERGE in VBS 2025”, 31st International Conference on Multimedia Modeling, Napa, Japan, January 8-10, 2025

S. Papadopoulos, K. Ioannidis, S. Vrochidis, I. Kompatsiaris and I. Patras, “Vision-Language Pretraining for Variable-shot Image Classification,” in 2025 International Conference on Multimedia Modeling, MMM 2025, Nara, Japan, January 8-10, 2025.

Pegia M., Jónsson, B. Þ., Moumtzidou, A., Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I., “Comparative Analysis of Learning-Based Approaches for Change Detection in Satellite Images”, IEEE Selected Topics in Applied Earth Observations and Remote Sensing, 2024

K. Gkountakos, K. Ioannidis, K. Demestichas, S. Vrochidis and I. Kompatsiaris, “A Comprehensive Review of Deep Learning-based Anomaly Detection Methods for Precision Agriculture”, in IEEE Access, 2024

V.R. Xefteris, A.C. Syropoulou, T. Pistola, P. Kasnesis, I. Poulios, A. Tsanousa, S. Symeonidis, S. Diplaris, K. Goulianas, P. Chatzimisios, S. Vrochidis, “Multimodal fusion of inertial sensors and single rgb camera data for 3d human pose estimation based on a hybrid lstm-random forest fusion network”, Internet of Things p. 101465, 2024. https://doi.org/10.1016/j.iot.2024.101465

iDriving aims to deliver a TRL 6 prototype based on seven key pillars, all designed to work synergistically to create an interactive, accurate and efficient solution to improve infrastructure safety of urban and secondary rural roads. (i) A Safety Criteria Catalogue (SCC) for all road users for secondary and urban roads. This establishes a benchmark for safety standards and KPIs, forming the foundational blueprint that guides all aspects of the iDriving system.

G. Koutidis, K. Loumponias, T. Tsikrika, S. Vrochidis and I. Kompatsiaris, “Towards AI-Driven Prediction of Terrorism Risk Based on the Analysis of Localized Web News,” in IEEE Access, vol. 12, pp. 167288-167299, 2024, https://doi.org/10.1109/ACCESS.2024.3485913

CARMA aims to co-create, through a user-centred iterative methodology involving a complementary set of end-users and social sciences and human (SSH) experts, a groundbreaking, modular and intuitive platform offering a complementary set of semi-autonomous and autonomous Unmanned Ground Vehicles (UGVs) capable of working in symbiosis with humans to support and supplement first responders and assist citizen in a wide range of disaster situations, including those with very low visibility. The project will build on the most advanced research results, including those from the INTREPID project, in the field of disaster robotics making them autonomous thanks to novel 3D radar-based environment mapping and analysis combined with Artificial Intelligence (AI) for enhanced path and mission planning as well as victim and threat detection.

K. Gkountakos, D. Galanopoulos, A. Leventakis, G. Tsionkis, K. Stavrothanasopoulos, K. Ioannidis, S. Vrochidis, V. Mezaris, I. Kompatsiaris, “ITI-CERTH participation in ActEV and AVS Tracks of TRECVID 2024”, Proc. TRECVID 2024 Workshop, Gaithersburg, MD, USA, Nov. 2024.

D.F. Mantsis, A. Moumtzidou, I. Lioumbas, I. Gialampoukidis, A. Christodoulou, A. Mentes, S. Vrochidis , and I. Kompatsiaris, “Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network,” Remote Sensing, vol. 16(20), pp. 3913, 2024. https://doi.org/10.3390/rs16203913