7 citations · 13 across the 22 of their papers we have counts for
10 papers · 1 filter
Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis
Vladislav Li, Ilias Siniosoglou, Thomai Karamitsou +6
Autonomous Vehicles (AVs) use natural images and videos as input to understand the real world by overlaying and inferring digital elements, facilitating proactive detection in an e…
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study
Ilias Siniosoglou, Vasileios Argyriou, George Fragulis +4
The time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the…
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection
Vladislav Li, Georgios Tsoumplekas, Ilias Siniosoglou +4
Current methods for low- and few-shot object detection have primarily focused on enhancing model performance for detecting objects. One common approach to achieve this is by combin…
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
Georgios Tsoumplekas, Ilias Siniosoglou, Vasileios Argyriou +2
The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, me…
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
Pavlos S. Bouzinis, Panagiotis Radoglou-Grammatikis, Ioannis Makris +5
Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model…
Autonomous AI-enabled Industrial Sorting Pipeline for Advanced Textile Recycling
Yannis Spyridis, Vasileios Argyriou, Antonios Sarigiannidis +2
The escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion in…
Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study
Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou +5
In the digital era, with escalating privacy concerns, it's imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual informa…
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches
Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou +6
In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there i…
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
Georgios Tsoumplekas, Vladislav Li, Ilias Siniosoglou +5
In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and comp…
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning
Georgios Tsoumplekas, Vladislav Li, Panagiotis Sarigiannidis +1
Despite the widespread success of deep learning, its intense requirements for vast amounts of data and extensive training make it impractical for various real-world applications wh…