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20152026
most citedDRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection

38 citations · 66 across the 13 of their papers we have counts for

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cs.CV2026

4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview

Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič +52

The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy an…

cs.CV2024

A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking

Alan Lukezic, Ziga Trojer, Jiri Matas +1

Performance of modern trackers degrades substantially on transparent objects compared to opaque objects. This is largely due to two distinct reasons. Transparent objects are unique…

cs.CV2023★ 1 cited

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

Benjamin Kiefer, Lojze Žust, Matej Kristan +46

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenge…

cs.CV2023

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

Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj

RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporati…

cs.CV2023★ 2 cited

LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark

Lojze Žust, Janez Perš, Matej Kristan

The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the fi…

cs.CV2023★ 1 cited

eWaSR -- an embedded-compute-ready maritime obstacle detection network

Matija Teršek, Lojze Žust, Matej Kristan

Maritime obstacle detection is critical for safe navigation of autonomous surface vehicles (ASVs). While the accuracy of image-based detection methods has advanced substantially, t…