3 papers
cs.CV2026
TestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model
Dimitrios Fotiou, Vasileios Mygdalis, Ioannis Pitas
Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Current methods for semantic se…
cs.CV2026
Concept-based explanations of Segmentation and Detection models in Natural Disaster Management
Samar Heydari, Jawher Said, Galip Ãmit Yolcu +7
Deep learning models for flood and wildfire segmentation and object detection enable precise, real-time disaster localization when deployed on embedded drone platforms. However, in…
cs.CV2026
Federated Unsupervised Semantic Segmentation
Evangelos Charalampakis, Vasileios Mygdalis, Ioannis Pitas
This work explores the application of Federated Learning (FL) to Unsupervised Semantic image Segmentation (USS). Recent USS methods extract pixel-level features using frozen visual…