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20242026
most citedA Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation

3 citations · 5 across the 10 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

CoDi -- an exemplar-conditioned diffusion model for low-shot counting

Grega Šuštar, Jer Pelhan, Alan Lukežič +1

Low-shot object counting addresses estimating the number of previously unobserved objects in an image using only few or no annotated test-time exemplars. A considerable challenge f…

cs.CV2025

Generalized-Scale Object Counting with Gradual Query Aggregation

Jer Pelhan, Alan Lukezic, Matej Kristan

Few-shot detection-based counters estimate the number of instances in the image specified only by a few test-time exemplars. A common approach to localize objects across multiple s…

cs.CV2025

Distractor-Aware Memory-Based Visual Object Tracking

Jovana Videnovic, Matej Kristan, Alan Lukezic

Recent emergence of memory-based video segmentation methods such as SAM2 has led to models with excellent performance in segmentation tasks, achieving leading results on numerous b…

cs.CV2025

What Holds Back Open-Vocabulary Segmentation?

Josip Šarić, Ivan Martinović, Matej Kristan +1

Standard segmentation setups are unable to deliver models that can recognize concepts outside the training taxonomy. Open-vocabulary approaches promise to close this gap through la…

cs.CV2025

DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation

Ivan Martinović, Josip Šarić, Marin Oršić +2

Pixel-level annotation is expensive and time-consuming. Semi-supervised segmentation methods address this challenge by learning models on few labeled images alongside a large corpu…

cs.CV2025

3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results

Benjamin Kiefer, Lojze Žust, Jon Muhovič +43

The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive…