6 citations · 12 across the 9 of their papers we have counts for
9 papers
DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting
Jer Pelhan, Alan Lukežič, Vitjan Zavrtanik +1
Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estima…
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…
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…
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…
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…
DSR -- A dual subspace re-projection network for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are pr…