Started a research internship at EPFL’s Visual Intelligence and Learning Lab (VILAB) through Summer@EPFL, working on multimodal foundation models, efficient visual tokenization, and generative modeling.
Hi, I’m Lazar! I’m a researcher working on computer vision and generative AI, with a particular interest in how models represent visual information and how we can make them more efficient and robust.
I finished my undergrad in Computer and Information Science at the University of Ljubljana, where I received the Dean’s Award for Outstanding Academic Achievement and worked with Prof. Marko Robnik-Šikonja on sarcasm detection in Slovene. I recently completed the Erasmus Mundus Joint Master in Artificial Intelligence, studying at Pompeu Fabra University, Radboud University, and the University of Ljubljana on a full Erasmus Mundus scholarship and graduating cum laude. For my master’s thesis, I worked with Prof. Matej Kristan and Dr. Alan Lukežič on memory-based methods for long-term visual point tracking.
Currently, I’m a research intern at EPFL’s VILAB, working with Prof. Amir Zamir on efficient visual tokenization for generative models. Previously, I interned at Microsoft and Teads, working on AI agents for understanding code and efficient training of prediction models.
I have broad interests and always enjoy a good discussion. If you’re working on something interesting or want to exchange ideas, feel free to reach out.
Started a research internship at EPFL’s Visual Intelligence and Learning Lab (VILAB) through Summer@EPFL, working on multimodal foundation models, efficient visual tokenization, and generative modeling.
Completed the Erasmus Mundus Joint Master in Artificial Intelligence with a 9.75/10.0 GPA, graduating cum laude. LinkedIn Post
Published XFeat Revisited: Reproducibility and Evaluation of a Lightweight Image Matcher in TMLR with a Reproducibility Certification.
Defended my master’s thesis at the University of Ljubljana with grade 10/10, presenting DeMeTer, a dense memory-based point tracker that achieved a 4.1% relative improvement in δavg over the strongest prior method while operating in real time. Master Thesis
Received the Best Poster Award in the Erasmus Mundus Joint Master in Artificial Intelligence for presenting my research on dense memory-based visual point tracking. Poster
We reproduce and re-evaluate XFeat, a lightweight image-matching model designed for efficient feature extraction on resource-constrained hardware. Our experiments recover its strong accuracy–efficiency trade-off on standard benchmarks, while architectural ablations clarify the role of its keypoint branch and skip connection. We also evaluate robustness under out-of-distribution and cross-modal image matching, where performance declines under severe modality shifts.
In Transactions on Machine Learning Research (TMLR), 2026
We create a Slovene sarcasm-detection dataset using machine translation and large language models, then evaluate cross-lingual transfer across monolingual and multilingual Transformer models. Our ensemble reaches an F1 score of 0.765.
In the Slovenian Conference on Artificial Intelligence (SCAI), 2024