OCSVM-based Evaluation Method for Generative Neural Networks. - Artúr István Károly; Márta Takács; Péter Galambos (IJCNN, 2019)

Optical flow-based segmentation of moving objects for mobile robot navigation using pre-trained deep learning models. - Artúr István Károly; Renáta Nagyné Elek; Tamás Haidegger; Károly Széll; Péter Galambos (SMC, 2019)

Simultaneous Planning of Grasp and Motion using Sample Regions and Gradient-Based Optimization. - Takayuki Murooka; Artúr István Károly; Felix von Drigalski; Yoshihisa Ijiri (CASE, 2020)

Towards Optical Flow Ego-motion Compensation for Moving Object Segmentation. - Renáta Nagyné Elek; Artúr István Károly; Tamás Haidegger; Péter Galambos (ROBOVIS, 2020)

Deep Learning in Robotics: Survey on Model Structures and Training Strategies. - Artúr István Károly; Péter Galambos; Jozsef Kuti; Imre J. Rudas (IEEE Trans. Syst. Man Cybern. Syst., 2021)

Increasing the Robustness of Deep Learning Models for Object Segmentation: A Framework for Blending Automatically Annotated Real and Synthetic Data. - Artúr István Károly; Sebestyén Tirczka; Huijun Gao; Imre J. Rudas; Péter Galambos (IEEE Trans. Cybern., 2024)

Fine-Tuning Large Language Models for Mobile Robot Navigation. - Dániel Horti; Péter Galambos; Artúr István Károly (SACI, 2025)

Digital Twin for Robotic Vision: A Blender-Based Approach. - Artúr István Károly; Ármin Károly; Péter Galambos (SACI, 2025)