Preprint

FZJ-2026-05160

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture

Guo, S.FZJ* ; Li, X. ; Arsova, B.FZJ* ; Scharr, H.FZJ* ; Salvi, S.

2026
arXiv

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Please use a persistent id in citations: doi:10.48550/ARXIV.2609.29460

Abstract: Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and imaging conditions. Exemplar boxes specify the target without category-specific retraining, and point predictions identify the individual instances contributing to the count. We introduce AgriCountDINO, a parameter-efficient exemplar-guided framework for joint counting and localization. It conditions frozen multiscale DINOv3 features on exemplar appearance and size, then progressively decodes them into target points. Missed-object recovery extends supervision to targets overlooked by initial matching, and exemplar-adaptive point NMS filters duplicate predictions according to exemplar scale. With 8.4M trainable parameters, approximately one-tenth of TasselNetV4's, AgriCountDINO achieves a three-shot MAE of 11.92 on the TPC-268 benchmark, reducing counting error by 9.7\% while providing individual target locations. Trained only on TPC-268, it achieves a zero-shot MAE of 14.25 on unseen generic object categories in FSC-147, improving upon the best compared zero-shot method by 6.0\% without target-domain training or fine-tuning.

Keyword(s): Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) ; FOS: Computer and information sciences

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2026

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