Vitjan Zavrtanik

According to our database, Vitjan Zavrtanik authored at least 11 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation.

Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj

Pattern Recognit. Lett., 2024

A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation.

Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan

CoRR, 2024

Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation.

Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Anomalous Sound Detection by Feature-Level Anomaly Simulation.

Vitjan Zavrtanik, Matija Marolt, Matej Kristan, Danijel Skocaj

Proceedings of the IEEE International Conference on Acoustics, 2024

TransFusion - A Transparency-Based Diffusion Model for Anomaly Detection.

Matic Fucka, Vitjan Zavrtanik, Danijel Skocaj

Proceedings of the Computer Vision - ECCV 2024, 2024

DAVE - A Detect-and-Verify Paradigm for Low-Shot Counting.

Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

A Low-Shot Object Counting Network With Iterative Prototype Adaptation.

Nikola Ðukic, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

DSR - A Dual Subspace Re-Projection Network for Surface Anomaly Detection.

Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

Reconstruction by inpainting for visual anomaly detection.

Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj

Pattern Recognit., 2021

DRÆM - A discriminatively trained reconstruction embedding for surface anomaly detection.

Vitjan Zavrtanik, Matej Kristan, Danijel Skocaj

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

A segmentation-based approach for polyp counting in the wild.

Vitjan Zavrtanik, Martin Vodopivec, Matej Kristan

Eng. Appl. Artif. Intell., 2020