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

Shengjie Guo; Xin Li; Borjana Arsova; Hanno Scharr; Silvio Salvi

cs.CVSep 24, 2026

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

Authors: Shengjie Guo, Xin Li, Borjana Arsova, Hanno Scharr, Silvio Salvi

Organizations: Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy · Institute of Bio- and Geosciences, Plant Sciences, Forschungszentrum Jülich, Jülich, Germany · Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milan, Italy · Institute for Advanced Simulation, Data Analytics and Machine Learning, Forschungszentrum Jülich, Jülich, Germany

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.

Figures & tables

Figure 1: Overview of AgriCountDINO. Exemplar appearance and size tokens condition frozen multiscale features through scale-specific query modules (SQMs). Progressive coarse-to-fine fusion produces a high-resolution representation for point scoring and offset prediction. Feature dimensions are shown for a $1024\times 1024$ input.

Figure 2: Missed-object recovery and exemplar-adaptive point NMS. (a) During training, unmatched targets are assigned to unique unused queries in their local neighborhoods. (b) During inference, nearby candidate points are suppressed using a radius determined by the exemplar scale.

Method	Shot	Validation			Test			Train. Params. $\downarrow$
Method	Shot	MAE $\downarrow$	RMSE $\downarrow$	$R^{2}\uparrow$	MAE $\downarrow$	RMSE $\downarrow$	$R^{2}\uparrow$	Train. Params. $\downarrow$
LOCA (ICCV’23) [ 19 ]	3	17.26	53.19	0.75	17.51	38.37	0.78	11.37M
CACViT (AAAI’24) [ 16 ]	3	16.63	42.49	0.82	22.04	41.79	0.73	99.77M
TasselNetV4 (ISPRS J.’26) [ 5 ]	3	13.20	43.93	0.83	22.95	51.36	0.67	84.67M
AgriCountDINO (Ours)	3	11.92	28.55	0.93	21.76	49.58	0.74	8.40M
LOCA (ICCV’23) [ 19 ]	1	17.19 $\pm$ 0.31	48.14 $\pm$ 2.19	0.80 $\pm$ 0.02	21.47 $\pm$ 0.29	42.36 $\pm$ 0.72	0.73 $\pm$ 0.01	11.37M

Table 1: Comparison with representative state-of-the-art methods on TPC-268. Best and second-best results under each shot setting are shown in bold and underline , respectively.

Method	Shot	G1 $\downarrow$	G2 $\downarrow$	G3 $\downarrow$	Prec. $\uparrow$	Rec. $\uparrow$
TasselNetV4 [ 5 ]	3	17.71	22.75	33.33	–	–
TasselNetV4 [ 5 ]	1	19.14	23.86	33.93	–	–
AgriCountDINO	3	14.92	18.64	25.37	.719	.729
AgriCountDINO	1	15.90	19.72	27.17	.713	.709

Table 2: Spatial counting and localization on TPC-268 validation. G1–G3 denote GAME1–GAME3.

Method	FSC-147 $\rightarrow$ FSC-147		TPC-268 $\rightarrow$ FSC-147
Method	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$
LOCA [ 19 ]	10.79	56.97	15.16	109.15
CACViT [ 16 ]	9.13	48.96	17.88	82.57
TasselNetV4 [ 5 ]	16.16	110.56	19.17	116.24
AgriCountDINO	11.62	85.07	14.25	106.88

Table 3: Three-shot in-domain and zero-shot TPC-268

\rightarrow

FSC-147 results.

Configuration	MAE $\downarrow$	G3 $\downarrow$	F1 $\uparrow$	Recall $\uparrow$
Supervised ConvNeXt-B prior	17.64	32.19	0.668	0.646
w/o scale-wise exemplar conditioning	15.12	31.78	0.669	0.632
w/o missed-object recovery	12.71	26.27	0.723	0.692
Full model	11.92	25.37	0.723	0.729

Table 4: Ablation study on the TPC-268 validation set under the 3-shot setting.

Figure 3: Qualitative comparison with TasselNetV4. Exemplar boxes are shown in the ground truth, and dots denote target locations. The top three rows are from TPC-268; the bottom two show zero-shot transfer to FSC-147.

Figure 4: Possible annotation discrepancies in TPC-268. Top: annotations and exemplar boxes; bottom: AgriCountDINO predictions. Red boxes highlight apparent discrepancies

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