RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars

Adriano D'Alessandro; Ali Mahdavi-Amiri; Ghassan Hamarneh

cs.CVAug 20, 2026

RECOUNT: Reference-guided Counting with Synthetic Visual Exemplars

Authors: Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh

Organizations: Simon Fraser University

Abstract

Text-guided zero-shot object counters excel at spatial localization but categorize poorly on novel or fine-grained classes: natural language is too coarse to fully specify visual identity, so they fail to separate visually similar distractors. Few-shot counters sidestep this with visual exemplars, but require manual annotations on every image. To resolve this dilemma, we introduce RECOUNT, a plug-and-play framework for image-guided zero-shot counting. Rather than specify a category with a text prompt, our key insight is to specify it visually, from a single off-scene reference image. However, we find that a lone reference image provides narrow coverage of a category's appearance and is unreliable across diverse scenes. We therefore repurpose a diffusion model as an automated contrastive data engine that expands the reference into a diverse exemplar gallery, supplying the discriminative detail that text cannot. RECOUNT preserves the class-agnostic proposals of any frozen counter and offloads categorization to a separate visual module (a frozen backbone with a lightweight head trained on this synthetic data) that matches each proposal against the target and distractor galleries. Applied to a frozen counter, RECOUNT attains the best zero-shot accuracy on both benchmarks, cutting counting error (MAE) by 55% on LookAlikes and 21% on PairTally relative to the strongest prior zero-shot counter.

Figures & tables

Figure 1: From a reference image to precise counts in mixed category scenes. We propose RECOUNT, which automatically turns a reference image into an unlimited supply of synthetic visual exemplars (SVEs). We use SVEs to create support sets that specify both what to count and what to reject. Applied as a lightweight filter over a frozen counter’s point proposals, RECOUNT pulls a single category out of multi-category scenes where language alone cannot. Green points are accepted, red points are rejected. Each panel pairs a query image with the target (top, green) and distractor (bottom, red) SVE galleries that drive the decision. Across these challenging scenes, RECOUNT recovers the exact count.

Figure 2: A New Approach to Inference-Time Category Specification. Existing class-agnostic counters require either per-image manual bounding-box annotations at test time ( top-left ) or text prompts too coarse to specify visual identity ( bottom-left ). In contrast, our framework ( right ) specifies a category from a single real reference image, provided for both the target and its distractors. A synthetic data engine expands these lone references into a dense, visually diverse exemplar support set. Generated once per category, the support set is reusable across any number of query images. By deferring categorization until after spatial localization, RECOUNT matches each localized proposal against this support set, isolating the target from its distractors without per-image human labeling.

Figure 3: Decoupling Dense Localization from Dense Categorization. Object counting requires solving two distinct tasks: dense localization and dense categorization. Traditional models trained on single-category benchmarks (e.g., FSC147) conflate these steps; they excel at localizing objects ( center ) but fail to separate visually similar or novel categories. Decoupling these operations treats localization as a class-agnostic geometry problem, leaving semantic boundaries to be resolved independently ( right ).

Figure 4: Fine-Grained Data Generation with Image References. Standard text-to-image models struggle to synthesize exact fine-grained details from text prompts alone. Given the prompt “Poblano peppers,” and a dense description, a text-only generation ( center ) defaults to producing generic green pepper traits. By conditioning on a single real reference image ( left ), we can design synthetic training data that capture domain-specific fine-grained visual features ( right ).

Figure 5: Automated Generation of a Fine-Grained Dense Categorization Training Dataset. Our data engine synthesizes dense training scenes by coupling real-world taxonomic relationships with generative image expansion. We initialize our categories by combining the iNaturalist taxonomy with manually seeded non-natural object classes. An LLM structurally parses this input to construct a massive, unified fine-grained taxonomy, which dictates image-fetching queries across diverse online repositories. The collected real-world reference images are filtered and then paired with LLM-generated scene templates, guiding a latent diffusion model to synthesize dense, visually complex scenes with accurate fine-grained object categories.

Figure 6: Generated Fine-Grained Training Groups. Sample outputs from our automated data engine showing three distinct semantic groups ( Asteraceae flower species, shellfish, butterfly species) containing visually similar but distinct siblings. By organizing dense, multi-object scenes into contrastive sibling subsets, this data provides the explicit training signal required for our downstream dense categorization module.

Figure 7: Contrastive Training Pipeline. We train the projection head $P_{\theta}$ on synthetic dense scenes, leaving the backbone and counter frozen. (Top) From a few real references per category, our engine generates a synthetic support set for a category $c$ : positive exemplars $\mathcal{G}^{+}_{c}$ depicting the target, and hard-negative exemplars $\mathcal{G}^{-}_{c}$ depicting its visually similar distractors. A frozen image encoder $\phi$ , a frozen counter, and the projector $P_{\theta}$ reduce every exemplar to a support feature. (Bottom) For a synthetic scene of $c$ , the frozen counter pseudo-labels each instance with a point and $\phi$ produces a feature grid; indexing the grid at these points yields per-object features, which $P_{\theta}$ refines through a learned residual. (Right) A multi-negative contrastive loss $\mathcal{L}_{\mathrm{contr}}$ pulls each refined per-object feature toward the positive support features and away from the negative support features. Only $P_{\theta}$ is trained.

Figure 8: Point Pooling for Synthetic Visual Exemplars. Each synthetic visual exemplar (SVE) in a support gallery is reduced to a single prototype. The frozen counter proposes a point on every instance in the exemplar; we sample the backbone ( $\phi$ ) feature grid at these points by bilinear interpolation, average the sampled features into one descriptor, and project it with the head $P_{\theta}$ to obtain the exemplar’s prototype. A support gallery is the set of these prototypes, one per exemplar.

Figure 9: Crop-and-Reprocess. The pre-trained vision backbones that we utilize emit a fixed $32\times 32$ patch grid, so in dense regions many instances occupy a single patch and cannot be meaningfully separated, causing the filter to miss or misjudge them ( left ). We detect each dense cluster, crop it ( middle ), and re-extract features at native resolution before re-categorizing the enclosed points ( right ), recovering instances the base grid could not resolve.

Method	Reference	Inference	$\text{A}\rightarrow\text{B}$		$\text{A}\rightarrow\text{C}$
Method	Reference	Inference	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$
PSeCo +FiGO ( 2025 )	Text	TTA	19.57	30.86	-	-
DAVE +FiGO ( 2025 )	Text	TTA	21.19	31.53	-	-
CountGD +FiGO ( 2025 )	Text	TTA	13.31	23.36	-	-
GrREC +FiGO ( 2025 )	Text	TTA	16.15	26.99	-	-
CountGD ( 2024 )	BBox	Few-Shot	-	-	46.67	70.85

Table 1: Cross-domain Transfer Performance Under Varying Inference Protocols. We evaluate cross-domain transfer from the single-category FSC-147 dataset (A) ( Ranjan et al., 2021 ) to two fine-grained, multi-category counting datasets: LookAlikes (B) ( D’Alessandro et al., 2025 ) and PairTally (C) ( Nguyen et al., 2025 ) . Notation

\text{A}\rightarrow\text{B/C}

denotes the training set (left) and test set (right). Reference modalities:

\oplus

and

\ominus

indicate the inclusion of positive and negative categories, respectively; BBox and Image represent bounding boxes from the target image and separate independent reference images; and +pseudo denotes text-selected pseudo-bounding boxes. Bold indicates the best score for each inference mode, and underline marks the overall best result. Note that LookAlikes is zero-shot only and cannot be evaluated in few-shot mode. Gray shading highlights our method.

Method	Reference	Negative Test		Mosaic test
Method	Reference	NMN $\downarrow$	PCCN $\uparrow$	CntP $\uparrow$	CntR $\uparrow$
TFPOC ( 2024 )	Text ⊕	0.75	66.04	0.69	0.85
DAVE ( 2024 )	Text ⊕	1.05	37.02	0.84	0.80
DAVE ( 2024 )	Text ${}^{\ominus}_{\oplus}$	0.08	97.62	0.84	0.80
CountGD++ ( 2026 )	Text ⊕ +pseudo	0.88	62.86	0.86	0.96
CountGD++ ( 2026 )	Text ${}^{\ominus}_{\oplus}$ +pseudo	0.07	97.99	0.90	0.96

Table 2: Prompt Adherence Results. We benchmark our method against other zero-shot methods using the PrACo dataset ( Ciampi et al., 2025 ) , which measures whether a model correctly counts the prompted category. The Negative Test prompts each image with every class and reports Normalized Mean of Negative (NMN), which is the mean of the count predicted for absent classes, normalized by the ground-truth count of the present class, and Positive Class Count Nearness (PCCN), which is the percentage of images whose positive-class count is closer to ground truth than its negative-class counts. The Mosaic Test stacks the target image with a distractor-class image and counts the target over the collage, reporting counting precision (CntP) and recall (CntR); Text = text-grounded cues (and, for CountGD++, text-synthesized exemplars); +pseudo = pseudo-exemplars detected on the query image; Image = a reference image that was not derived from the query image. Best per column in bold .

Figure 10: Qualitative results on LookAlikes . Each panel shows our filter’s per-point decisions for a target category in a scene containing co-present lookalikes. In this oracle setting, the filter is applied to ground-truth points to isolate categorization from detection. Markers encode decisions: a green circle is a target point accepted (true positive), a red circle is a target point rejected (false negative), a red $\times$ is a distractor point rejected (true negative), and a green $\times$ is a distractor point accepted (false positive). We report recall (R), false-positive rate (FP), and F1 at operating threshold $\delta{=}0$ . Examples span 24 fine-grained categories across LookAlikes , showing the filter accepts the target while rejecting near-identical siblings.

Backbone	Head	Reference	Metrics
Backbone	Head	Reference	Recall $\uparrow$	FPR $\downarrow$	AUC $\uparrow$
DINOv3	dino.txt	Text	0.535	0.086	0.804
DINOv3	None	Reference Only	0.624	0.083	0.809
DINOv3	None	SVE Gallery	0.849	0.116	0.913
DINOv3	RECOUNT	SVE Gallery	0.870	0.096	0.929
DINOv2	None	Reference Only	0.542	0.065	0.765

Table 3: Ablation over architectural components and reference modalities. Evaluating the impact of vision backbone architecture, the RECOUNT head, and reference modality on performance. Evaluated using the Lookalikes dataset with oracle localizations.

Number of SVEs	Recall $\uparrow$	FPR $\downarrow$	AUC $\uparrow$
1	0.818	0.097	0.914
3	0.833	0.090	0.914
6	0.847	0.083	0.918
12	0.870	0.096	0.929
24	0.878	0.096	0.924

Table 4: Impact of Synthetic Visual Exemplar (SVE) Gallery Size on Performance. The number of SVEs used in the positive and negative galleries are variable, and so we ablate over the number of SVEs used and it’s impact on performance. We find performance saturates when the gallery size is restricted to 12 SVEs per-category. Evaluated using the Lookalikes dataset with oracle localizations.

Operation	Recall $\uparrow$	FPR $\downarrow$	AUC $\uparrow$
Whole-Image (GAP, all patches)	0.746	0.123	0.890
Per-Instance Pseudo-Detections	0.825	0.098	0.916
Pooled Pseudo-Detection	0.870	0.096	0.929

Table 5: SVE Pooling Operation Ablation. SVE galleries consist of synthetic images whose visual features originate from the underlying RECOUNT module. Utilizing an SVE image as part of gallery for specifying a category necessitates a mechanism to pool its feature grid. We compare global average patch pooling against point-feature pooling via pseudo-detections, where point coordinates are extracted per image using an off-the-shelf counter (CountGD). These coordinates are indexed into the visual feature patch grid to extract interpolated point features. Evaluated using the Lookalikes dataset with oracle localizations.

Visual Exemplar	Crowding	Recall $\uparrow$	FPR $\downarrow$	AUC $\uparrow$
Synth.	$\times$	0.735	0.096	0.882
Synth.	✓	0.870	0.098	0.929
Real	✓	0.925	0.101	0.930

Table 6: Evaluating Exemplar Source and Quality. We evaluate the performance gap between real and synthetic visual exemplars while holding all other parameters fixed; only the per-category exemplar gallery varies. To isolate the synthetic-to-real domain gap, we also compare synthetic exemplars containing a single canonical object (no crowding) against synthetic exemplars specifically prompted to contain crowds. Evaluated using the Lookalikes dataset with oracle localizations.

Reference Used
Train	Infer	Recall $\uparrow$	FPR $\downarrow$	AUC $\uparrow$
$\times$	$\times$	0.727	0.121	0.887
✓	$\times$	0.807	0.127	0.903
✓	✓	0.870	0.096	0.929

Table 7: Effect of Reference Usage During Training and Inference. Ablation evaluating performance across different combinations of train- and test-time references to assess the impact of reference guidance. Evaluated using the Lookalikes dataset with oracle localizations.

Strategy	Recall $\uparrow$	FPR $\downarrow$	AUC $\uparrow$	Recall-Contam. $\uparrow$	FPR-Contam. $\downarrow$
Base	0.870	0.096	0.929	0.620	0.329
Crop	0.883	0.099	0.949	0.753	0.368
$\Delta$	+0.013	+0.003	+0.020	+0.133	+0.039

Table 8: Benefits of Crop-and-Reprocess. We evaluate the performance of our model when dense regions are adaptively cropped and then passed back through the model. We speculate that contamination from neighboring points impacts the separability of a target point. To evaluate this, we also report contaminated recall, which takes the original 32×32 grid from DINOv3, and calculates recall for all target points that have a distractor sibling within a 3×3 grid around that point. Evaluated using the Lookalikes dataset with oracle localizations.

$d$	Neighborhood	$N$	Recall $\uparrow$
$d$	Neighborhood	$N$	Base	Crop	$\Delta$
0	Same patch ( $1\times 1$ )	636	0.5466	0.733	$+0.169$
1	$3\times 3$ shell	4,055	0.717	0.818	$+0.101$
2	$5\times 5$ shell	3,905	0.844	0.886	$+0.042$
3	$7\times 7$ shell	3,000	0.891	0.914	$+0.023$
4	$9\times 9$ shell	2,445	0.909	0.928	$+0.019$

Table 9: Spatial contamination. We evaluate recall as a function of Chebyshev distance

d

(in grid cells) to the nearest distractor object. We show that dense categorization breaks down when distractor objects share a patch with the target object. However, there also appears to be a degradation in performance when adjacent distractor objects multiple patches away. Evaluated using the Lookalikes dataset with oracle localizations.

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This paper presents CountEx, a discriminative visual counting framework designed to address a key limitation of existing prompt-based methods: the inability to explicitly exclude visually similar distractors. While current approaches allow users to specify what to count via inclusion prompts, they often struggle in cluttered scenes with confusable object categories, leading to ambiguity and overcounting. CountEx enables users to express both inclusion and exclusion intent, specifying what to count and what to ignore, through multimodal prompts including natural language descriptions and optional visual exemplars. At the core of CountEx is a novel Discriminative Query Refinement module, which jointly reasons over inclusion and exclusion cues by first identifying shared visual features, then isolating exclusion-specific patterns, and finally applying selective suppression to refine the counting query. To support systematic evaluation of fine-grained counting methods, we introduce CoCount, a benchmark comprising 1,780 videos and 10,086 annotated frames across 97 category pairs. Experiments show that CountEx achieves substantial improvements over state-of-the-art methods for counting objects from both known and novel categories. The data and code are available at https://github.com/bbvisual/CountEx.

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Zero-shot object counting (ZOC) aims to count instances of arbitrary object categories specified only through textual prompts. Recent training-free approaches leverage foundation models such as SAM to reformulate counting as a prompt-driven segmentation task, eliminating the need for costly counting-specific training data with point-level annotations. More recently, SAM3 introduced promptable concept segmentation, enabling the zero-shot segmentation of all instances corresponding to a text-defined concept. However, SAM3 struggles in densely populated scenes containing numerous small objects, where limited image resolution and insufficient attention to target-relevant regions often lead to missed instances and poor instance separation, hindering accurate object counting. To address this limitation, we propose AdaCount, a training-free framework for ZOC based on similarity-guided spatial and feature adaptation. AdaCount first estimates a prototype-driven similarity map that identifies target-relevant regions. This similarity map subsequently guides two complementary adaptations: (i) similarity-guided spatial warping, which reallocates image resolution toward target instances, and (ii) feature modulation, which amplifies target-relevant encoder representations. Together, these adaptations enable SAM3 to devote greater representational capacity to target-relevant regions while preserving global image context, without requiring any model retraining. Extensive experiments across six diverse counting benchmarks establish AdaCount as a new SOTA among training-free ZOC approaches.

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Mohamed Bin Zayed University of Artificial Intelligence

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Abstract

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