Vitjan Zavrtanik Researcher email@example.com Research Visual anomaly detection This research focuses on the development of unsupervised visual anomaly detection methods. Trained on anomaly-free samples only, these methods attempt to remove the need for a difficult acquisition of a diverse set of anomalous objects while aiming to match the performance of supervised methods. Dense object counting in underwater imagery The main goal of this research is development of computer-vision-based automated counters applicable underwater imagery. Such counters are crucial for processing extremely large datasets, vastly reducing the required manual labor and facilitating census orders of magnitude grater than what is possible with standard techniques. The methods leverage learning on specific type of images to maximize a task-specific detection performance.