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AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture

arXiv · AI, language, vision and robotics · article · Sep 24, 2026 · UTC

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. Mi

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