mlsanityData to 14 Oct 2026

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

arXiv 2609.29460 ↗First posted 24 Sep 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 19 references that are in the map. First posted on arXiv 24 Sep 2026.

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References · 17

  1. 2017Mask R-CNNcited by 7,277
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2022A ConvNet for the 2020scited by 3,579
  4. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  5. 2025DINOv3cited by 1,414
  6. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 1,039
  7. 2021Rethinking Counting and Localization in Crowds:A Purely Point-Based Frameworkcited by 152
  8. 2022Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Countingcited by 80
  9. 2022A Low-Shot Object Counting Network With Iterative Prototype Adaptationcited by 65
  10. 2022Few-shot Object Counting and Detectioncited by 55
  11. 2024DAVE -- A Detect-and-Verify Paradigm for Low-Shot Countingcited by 49
  12. 2017TasselNet: Counting maize tassels in the wild via local counts regression networkcited by 26
5 more
  1. 2023Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Countingcited by 25
  2. 2024A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentationcited by 21
  3. 2025Generalized-Scale Object Counting with Gradual Query Aggregationcited by 5
  4. 2025TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant countingcited by 2
  5. 2026Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Speciescited by 2

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