A segmentation-based approach for polyp counting in the wild. - Vitjan Zavrtanik; Martin Vodopivec; Matej Kristan (Eng. Appl. Artif. Intell., 2020)

DRÆM - A discriminatively trained reconstruction embedding for surface anomaly detection. - Vitjan Zavrtanik; Matej Kristan; Danijel Skocaj (ICCV, 2021)

Reconstruction by inpainting for visual anomaly detection. - Vitjan Zavrtanik; Matej Kristan; Danijel Skocaj (Pattern Recognit., 2021)

DSR - A Dual Subspace Re-Projection Network for Surface Anomaly Detection. - Vitjan Zavrtanik; Matej Kristan; Danijel Skocaj (ECCV (31), 2022)

A Low-Shot Object Counting Network With Iterative Prototype Adaptation. - Nikola Ðukic; Alan Lukezic; Vitjan Zavrtanik; Matej Kristan (ICCV, 2023)

DAVE - A Detect-and-Verify Paradigm for Low-Shot Counting. - Jer Pelhan; Alan Lukezic; Vitjan Zavrtanik; Matej Kristan (CVPR, 2024)

TransFusion - A Transparency-Based Diffusion Model for Anomaly Detection. - Matic Fucka; Vitjan Zavrtanik; Danijel Skocaj (ECCV (35), 2024)

Anomalous Sound Detection by Feature-Level Anomaly Simulation. - Vitjan Zavrtanik; Matija Marolt; Matej Kristan; Danijel Skocaj (ICASSP, 2024)

A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation. - Jer Pelhan; Alan Lukezic; Vitjan Zavrtanik; Matej Kristan (NeurIPS, 2024)

Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation. - Vitjan Zavrtanik; Matej Kristan; Danijel Skocaj (WACV, 2024)

Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation. - Vitjan Zavrtanik; Matej Kristan; Danijel Skocaj (Pattern Recognit. Lett., 2024)

Robustness of unsupervised methods for image surface-anomaly detection. - Jakob Bozic; Matic Fucka; Vitjan Zavrtanik; Danijel Skocaj (Pattern Anal. Appl., 2025)