mlsanityData to 7 Oct 2026

RS-OVC: Open-Vocabulary Counting for Remote-Sensing Data

arXiv 2604.08704 ↗First posted 9 Apr 2026cs.CVcited by 1

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Abstract (arXiv)

Object-Counting for remote-sensing (RS) imagery is attracting increasing research interest due to its crucial role in a wide and diverse set of applications. While several promising methods for RS object-counting have been proposed, existing methods focus on a closed, pre-defined set of object classes. This limitation necessitates costly re-annotation and model re-training to adapt current approaches for counting of novel objects that have not been seen during training, and severely inhibits their application in dynamic, real-world monitoring scenarios. To address this gap, in this work we propose RS-OVC - the first Open Vocabulary Counting (OVC) model for Remote-Sensing and aerial imagery. We show that our model is capable of accurate counting of novel object classes, that were unseen during training, based solely on textual and/or visual conditioning.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 9 Apr 2026.

Builds on

15 of 44 references.

References · 15

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3 more
  1. 2024NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Imagescited by 14
  2. 2023Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Reviewcited by 8
  3. 2025Mamba-MOC: A Multicategory Remote Object Counting via State Space Modelcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challengescited by 0