DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting

Xuan Cuong Ngo

cs.CVSep 14, 2026

DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting

Authors: Xuan Cuong Ngo

Organizations: University of Arkansas, USA

Abstract

Zero-shot object counting aims to estimate the number of objects specified by a text query without category-specific training. Recent approaches primarily rely on density regression or detection-style instance prediction. While effective, density-based models often suffer from spatial ambiguity and background leakage due to weakly regulated mass allocation, leading to fragmented or part-biased representations that increase counting error in complex scenes. In this work, we propose an instance-aware dual-decoder framework that structurally couples density and point representations for zero-shot object counting. Instead of treating density estimation as independent pixel-wise regression, we interpret it as a structured mass allocation problem over a latent set of object instances. Predicted instance centers induce a soft instance-wise decomposition of the density map, upon which we enforce two geometric constraints: (1) per-instance mass conservation, ensuring each object contributes approximately one unit of density mass, and (2) center-of-mass alignment, encouraging each density component to concentrate around its corresponding predicted center. These constraints introduce instance-level geometric consistency and lead to more accurate mass allocation, thereby reducing counting error. Extensive experiments on FSC-147, PUCPR+, and CARPK show that our approach consistently reduces counting error and establishes new state-of-the-art performance in zero-shot object counting.

Figures & tables

Figure 1: Density map visualizations. CLIP-Count [ 8 ] and VLCounter [ 9 ] are prone to false positive predictions, while our model mitigates this issue by leveraging spatial constraints from the point prediction branch, resulting in more controlled density estimates.

Figure 2: Overview of DualCount. Visual and textual features are extracted by the image encoder $E_{v}$ and text encoder $E_{t}$ , enhanced via cross-modal interaction $\mathcal{E}$ , and fused by $\mathcal{F}$ to produce a fused feature map. A dual-decoder head ( $D_{\rho}$ and $D_{c}$ ) predicts the density map and instance centers.

Figure 3: Qualitative comparison between DualCount and density-based zero-shot counting baselines, including VLCounter [ 9 ] , CLIP-Count [ 8 ] , and CounTX [ 1 ] .

Scheme	Method	Venue	Shot	Val Set		Test Set
Scheme	Method	Venue	Shot	MAE	RMSE	MAE	RMSE
Few-shot	FamNet [ 21 ]	CVPR’21	3	24.32	70.94	22.56	101.54
	BMNet [ 22 ]	CVPR’22	3	15.74	58.53	14.62	91.83
	LOCA [ 30 ]	ICCV’23	3	10.24	32.56	10.97	56.97
	SAM [ 23 ]	WACV’24	3	-	-	19.95	132.16
	PseCo [ 11 ]	CVPR’24	3	15.31	68.34	13.05	112.86

Table 1: Quantitative comparison on FSC147. We report MAE and RMSE under different settings: few-shot, reference-less, and zero-shot. In each setting, the best results are highlighted in bold while the runner-up is highlighted in underline .

Dataset	Method	Venue	MAE $\downarrow$	RMSE $\downarrow$
CARPK	CLIP-Count [ 8 ]	ACM MM’23	11.96	16.61
	CounTX [ 1 ]	BMVC’23	8.13	10.87
	T2ICount [ 19 ]	CVPR’25	8.61	13.47
	CountGD [ 2 ]	NeurIPS’24	3.83	5.41
	DualCount (Ours)	–	7.95	9.78
PUCPR+	CLIP-Count [ 8 ]	ACM MM’23	55.82	91.23

Table 2: Performance comparison between DualCount and zero-shot counting baselines on CARPK and PUCPR+.

$\mathcal{L}_{ot}$	$\mathcal{L}_{cst}$	$\mathcal{L}_{consistent}$	$\mathcal{L}_{mass}$	$\mathcal{L}_{align}$	MAE	RMSE
$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$	11.32	97.16
$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$	$\times$	12.84	100.45
$\checkmark$	$\checkmark$	$\checkmark$	$\times$	$\checkmark$	13.05	100.98
$\checkmark$	$\checkmark$	$\times$	$\checkmark$	$\checkmark$	14.25	102.17
$\checkmark$	$\times$	$\checkmark$	$\checkmark$	$\checkmark$	14.49	103.11

Table 3: Ablation study on the proposed loss components.

\checkmark

indicates the loss is applied during training, while

\times

indicates it is removed. Test MAE and RMSE are reported for each variant.

Enhancer	Density Decoder	Point Decoder	Backbone	MAE $\downarrow$	RMSE $\downarrow$
$\checkmark$	$\checkmark$	$\times$	SwinT+BERT	14.12	103.87
$\checkmark$	$\times$	$\checkmark$	SwinT+BERT	14.54	105.29
$\times$	$\checkmark$	$\checkmark$	SwinT+BERT	13.11	101.08
$\checkmark$	$\checkmark$	$\checkmark$	CLIP	12.33	99.78
$\checkmark$	$\checkmark$	$\checkmark$	SwinT+BERT	11.32	97.16

Table 4: Ablation study on architectural components of DualCount. We evaluate the impact of the feature enhancer, decoder design, and backbone choice.

Object Counting

Object counting estimates the number and locations of target instances in images or video, from dense crowds to fine-grained categories.

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AdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Counting

Muhammad Ibraheem Siddiqui, Muhammad Haris Khan

Mohamed Bin Zayed University of Artificial Intelligence

Test-Time Adaptation Vision Foundation Model Adaptation Object Counting

Open paper AskPDF arXiv

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.

Abstract

Object Counting

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