Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting

Fan Chen; Shuyin Xia; Yi Wang

cs.CVMar 25, 2026

Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting

Authors: Fan Chen, Shuyin Xia, Yi Wang

Organizations: Chongqing University of Posts and Telecommunications, Chongqing, China · Chongqing Ant Consumer Finance Co., Ltd., Chongqing, China

Abstract

Single-source domain generalization for crowd counting is highly challenging because a single labeled source domain may contain heterogeneous latent domains, while unseen target domains often exhibit severe distribution shifts. A central issue is stable latent domain discovery: directly performing flat clustering on evolving sample-level latent features is easily disturbed by feature noise, outliers, and representation drift, leading to unreliable pseudo-domain assignments and weakened domain-structured learning. To address this problem, we propose a granular ball guided stable latent domain discovery framework for domain-general crowd counting. The proposed method first groups samples into compact local granular balls and then clusters granular ball centers as representatives to infer pseudo-domains, thereby converting direct sample-level clustering into a hierarchical representative-based clustering process. This design produces more stable and semantically consistent pseudo-domain assignments. On top of the discovered latent domains, we develop a two-branch learning framework that improves transferable semantic representations via semantic codebook re-encoding and captures domain-specific appearance variations through a style branch, thereby alleviating semantic--style entanglement under domain shifts. Extensive experiments on ShanghaiTech A/B, UCF_QNRF, and NWPU-Crowd under a strict no-adaptation protocol verify the effectiveness of the proposed method and show strong generalization ability, especially in transfer settings with large domain gaps.

Figures & tables

Figure 1: Illustration of three settings under domain shift: fully supervised learning, domain adaptation, and domain generalization.

Figure 2: Overall framework of the proposed method, consisting of semantic codebook re-encoding, style-branch regularization, and granular ball guided stable latent domain discovery for robust density regression.

Figure 3: Hierarchical division and merging of a weighted granular ball set (WGBS) for pseudo-domain discovery. Starting from the initial ball set $S^{(0)}$ , parent balls are iteratively split by weighted 2-means and accepted only when the child compactness decreases sufficiently. After no further split is accepted, $K$ -means is performed on granular ball centers to obtain pseudo-domains.

Source		SHA				SHB				QNRF
Target		SHB		QNRF		SHA		QNRF		SHA		SHB
Method	Publication	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE
DMCount [ 3 ]	NeurIPS’20	23.1	34.9	134.4	252.1	143.9	239.6	203.0	386.1	73.4	135.1	14.3	27.5
D2CNet [ 38 ]	TIP’21	21.6	34.6	126.8	245.5	164.5	286.4	267.5	486.0	–	–	–	–
SASNet [ 39 ]	AAAI’21	21.3	33.2	211.2	418.6	132.4	225.6	273.5	481.3	73.9	116.4	13.0	22.1
MAN [ 40 ]	CVPR’22	22.1	32.8	138.8	266.3	133.6	255.6	209.4	378.8	67.1	122.1	12.5	22.2

Table 1: Comparison with state-of-the-art no-adaptation methods under different source

\rightarrow

target cross-domain transfer settings (MAE/MSE). Venue denotes the publication venue of each method. Results are grouped by the source training set (SHA, SHB, or QNRF) and evaluated on the remaining unseen datasets. Bold values denote the best results.

Source	SHA		SHB		SHA+SHB
Target	NWPU
Method	MAE	MSE	MAE	MSE	MAE	MSE
DMCount [ 3 ]	146.9	563.8	191.6	747.4	144.6	592.8
SASNet [ 39 ]	158.8	588.0	195.7	716.8	155.3	583.6
MAN [ 40 ]	148.2	586.5	193.6	802.5	147.8	605.3
DGCC [ 18 ]	143.1	567.6	175.0	688.6	139.6	553.6

Table 2: Additional evaluation on NWPU-Crowd under a no-adaptation setting. Models are trained on SHA, SHB, or their union, and directly tested on NWPU.

Figure 4: Qualitative comparison on UCF_QNRF under the no-adaptation setting. From left to right: input image, GT density map, and the predictions of Ours, DGCC, UGSDA. The numbers denote crowd counts obtained by integrating the density maps.

Source	SHA				SHB				UCF_QNRF
Target	SHB		QNRF		SHA		QNRF		SHA		SHB
Method	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE
Random partition	12.8	23.4	119.4	218.4	120.2	210.2	169.2	307.1	68.6	114.6	14.3	27.0
K-means	12.9	24.4	119.8	219.6	122.7	220.2	166.1	295.3	68.0	111.3	13.5	25.3
Ours	11.1	15.7	108.0	197.4	97.5	171.7	160.3	298.8	64.1	103.5	13.0	21.5

Table 3: Ablation on pseudo-domain partitioning (MAE/MSE; lower is better). We fix

K

for a clean module ablation (

K{=}4

on SHA,

K{=}3

on SHB, and

K{=}6

on UCF_QNRF); the choice of

K

is discussed in the next subsection. Best results are highlighted in boldface.

Dataset	Method	$\Delta_{\mathrm{med}}\uparrow$	$\sigma_{\mathrm{med}}\uparrow$
SHA	Random	90.8	33.8
	K-means	270.9	110.2
	Ours	278.0	112.7
SHB	Random	20.7	8.5
	K-means	92.9	41.8
	Ours	110.2	46.3

Table 4: GT-count stratification of pseudo-domains. For each method, we compute the median GT count

m_{d}

for each pseudo-domain and report the range

\Delta_{\mathrm{med}}=\max(m_{d})-\min(m_{d})

and the standard deviation

\sigma_{\mathrm{med}}

across the

K

medians.

Source	SHA				SHB
Target	SHB		QNRF		SHA		QNRF
$K$	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE
3	12.8	24.3	120.9	218.1	97.5	171.7	160.3	298.8
4	11.1	15.7	108.0	197.4	119.3	202.9	172.7	306.4
5	12.6	23.7	118.3	215.6	117.4	200.7	177.6	322.0

Table 5: Effect of the number of pseudo-domains

K

under the single-source domain-generalization setting.

Source	SHA				SHB				QNRF
Target	SHB		QNRF		SHA		QNRF		SHA		SHB
Method	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE
baseline	14.8	27.7	134.5	230.5	132.6	229.7	190.6	305.9	88.2	145.0	18.8	35.6
+SCR	13.8	27.2	123.8	228.6	121.7	203.2	171.4	298.9	69.9	116.7	15.4	28.7
+SBR (Ours)	11.1	15.7	108.0	197.4	97.5	171.7	160.3	298.8	64.1	103.5	13.0	21.5
w/o $\mathcal{L}_{sem}$	13.9	27.0	117.8	214.1	119.3	214.8	172.7	306.4	67.2	111.0	14.1	26.5

Table 6: Ablation study of the core components (SCR and SBR) and the SBR regularizers under the single-source domain-generalization setting (without target-domain adaptation).

Crowd Counting

Crowd counting estimates how many people appear in images or video, supporting occupancy monitoring, safety planning, and crowd management.

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Abstract

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