Transfer learning for leveraging computer vision in infrastructure maintenance. - Mateusz Zarski; Bartosz Wójcik; Jaroslaw Adam Miszczak (CoRR, 2020)

Hard hat wearing detection based on head keypoint localization. - Bartosz Wójcik; Mateusz Zarski; Kamil Ksiazek; Jaroslaw Adam Miszczak; Miroslaw J. Skibniewski (CoRR, 2021)

KrakN: Transfer Learning framework and dataset for infrastructure thin crack detection. - Mateusz Zarski; Bartosz Wójcik; Jaroslaw Adam Miszczak (SoftwareX, 2021)

Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset. - Mateusz Zarski; Bartosz Wójcik; Jaroslaw Adam Miszczak; Bartlomiej Blachowski; Mariusz Ostrowski (IEEE Access, 2022)

Finicky transfer learning - A method of pruning convolutional neural networks for cracks classification on edge devices. - Mateusz Zarski; Bartosz Wójcik; Kamil Ksiazek; Jaroslaw Adam Miszczak (Comput. Aided Civ. Infrastructure Eng., 2022)

The Effectiveness of World Models for Continual Reinforcement Learning. - Samuel Kessler; Mateusz Ostaszewski; Michal Pawel Bortkiewicz; Mateusz Zarski; Maciej Wolczyk; Jack Parker-Holder; Stephen J. Roberts; Piotr Milos (CoLLAs, 2023)

Multi-Step Feature Fusion for Natural Disaster Damage Assessment on Satellite Images. - Mateusz Zarski; Jaroslaw Adam Miszczak (IEEE Access, 2024)

Balancing Performance and Scalability of Demand Forecasting ML Models. - Mateusz Zarski; Slawomir Nowaczyk (IDA, 2025)

dpVision: Environment for multimodal images. - Dariusz Pojda; Mateusz Zarski; Agnieszka Anna Tomaka; Leszek Luchowski (SoftwareX, 2025)

Semantic Depth Matters: Explaining Errors of Deep Vision Networks through Perceived Class Similarities. - Katarzyna Filus; Michal Romaszewski; Mateusz Zarski (CoRR, 2025)

Beyond Accuracy: Uncovering the Role of Similarity Perception and its Alignment with Semantics in Supervised Learning. - Katarzyna Filus; Mateusz Zarski (CoRR, 2025)