9 citations · 16 across the 11 of their papers we have counts for
12 papers
Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception
Spyridon Loukovitis, Anastasios Arsenos, Vasileios Karampinis +1
Open-set detection is crucial for robust UAV autonomy in air-to-air object detection under real-world conditions. Traditional closed-set detectors degrade significantly under domai…
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
Dimitrios Kollias, Anastasios Arsenos, James Wingate +1
This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medi…
Complex Style Image Transformations for Domain Generalization in Medical Images
Nikolaos Spanos, Anastasios Arsenos, Paraskevi-Antonia Theofilou +3
The absence of well-structured large datasets in medical computer vision results in decreased performance of automated systems and, especially, of deep learning models. Domain gene…
Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air Visual Object Detection
Anastasios Arsenos, Vasileios Karampinis, Evangelos Petrongonas +4
The main barrier to achieving fully autonomous flights lies in autonomous aircraft navigation. Managing non-cooperative traffic presents the most important challenge in this proble…
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation
Vasileios Karampinis, Anastasios Arsenos, Orfeas Filippopoulos +5
In the last twenty years, unmanned aerial vehicles (UAVs) have garnered growing interest due to their expanding applications in both military and civilian domains. Detecting non-co…
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation
Anastasios Arsenos, Dimitrios Kollias, Evangelos Petrongonas +2
In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when c…
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
Demetris Gerogiannis, Anastasios Arsenos, Dimitrios Kollias +2
Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities. In t…
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-…
A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using…
MMA-MRNNet: Harnessing Multiple Models of Affect and Dynamic Masked RNN for Precise Facial Expression Intensity Estimation
Dimitrios Kollias, Andreas Psaroudakis, Anastasios Arsenos +4
This paper presents MMA-MRNNet, a novel deep learning architecture for dynamic multi-output Facial Expression Intensity Estimation (FEIE) from video data. Traditional approaches to…
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision…
MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
Dimitrios Kollias, Anastasios Arsenos, Levon Soukissian +1
Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for…