QdaVPR: A novel query-based domain-agnostic model for visual place recognition

Shanshan Wan; Lai Kang; Yingmei Wei; Tianrui Shen; Haixuan Wang; Chao Zuo

cs.CVMar 8, 2026

QdaVPR: A novel query-based domain-agnostic model for visual place recognition

Authors: Shanshan Wan, Lai Kang, Yingmei Wei, Tianrui Shen, Haixuan Wang, Chao Zuo

Organizations: College of Systems Engineering and the Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, Hunan 410073, China

Abstract

Visual place recognition (VPR) aiming at predicting the location of an image based solely on its visual features is a fundamental task in robotics and autonomous systems. Domain variation remains one of the main challenges in VPR and is relatively unexplored. Existing VPR models attempt to achieve domain agnosticism either by training on large-scale datasets that inherently contain some domain variations, or by being specifically adapted to particular target domains. In practice, the former lacks explicit domain supervision, while the latter generalizes poorly to unseen domain shifts. This paper proposes a novel query-based domain-agnostic VPR model called QdaVPR. First, a dual-level adversarial learning framework is designed to encourage domain invariance for both the query features forming the global descriptor and the image features from which these query features are derived. Then, a triplet supervision based on query combinations is designed to enhance the discriminative power of the global descriptors. To support the learning process, we augment a large-scale VPR dataset using style transfer methods, generating various synthetic domains with corresponding domain labels as auxiliary supervision. Extensive experiments show that QdaVPR achieves state-of-the-art performance on multiple VPR benchmarks with significant domain variations. Specifically, it attains the best Recall@1 and Recall@10 on nearly all test scenarios: 93.5%/98.6% on Nordland (seasonal changes), 97.5%/99.0% on Tokyo24/7 (day-night transitions), and the highest Recall@1 across almost all weather conditions on the SVOX dataset. Our code will be released at https://github.com/shuimushan/QdaVPR.

Figures & tables

Fig. 1: Overview of the domain-agnostic VPR models. (a) Most models acquire some domain agnosticism through training on large-scale datasets. (b) Some VPR models are specially designed to be robust to target domains but generalize poorly to unseen domains. (c) Our model achieves domain agnosticism via a novel dual-level adversarial learning framework, which makes it more robust to VPR tasks under domain shift.

Fig. 2: Overview of the QdaVPR model. (a) The base architecture. Regardless of whether the input image is from the original GSV-cities or the generated six-domain synthetic GSV-cities datasets, this architecture outputs a global descriptor and $N_{c}$ query combinations. (b) The framework for dual-level adversarial learning. Each of the $L\times M$ query features and the $L$ domain features (extracted from the $L$ image features) is fed into a domain discriminator, producing output logits with six values. These values predict the specific domain of the input image. Note that output logits are generated only when the input image is from the generated six-domain dataset; otherwise, the outputs are as shown in (a). During inference, the red blocks are discarded, and only the global descriptor is output. The channel dimension is omitted from the figure for clarity. See text for details.

Fig. 3: Mutual reinforcement in the dual-level adversarial learning framework. Query features improve image features’ domain agnosticism through negative gradient flow, and image features reciprocally produce domain-agnostic query features via the framework flow. The symbols are defined in the text.

Dataset	Inquiry/Database	Scenery	Domain Shift
Pitts30k-test [ 51 ]	6816/10000	Urban	None.
Tokyo24/7 [ 24 ]	315/75984	Urban	Day/Night.
MSLS-val [ 36 ]	740/18871	Urban, Suburban	Day/Night, season and weather.
AmsterTime [ 52 ]	1231/1231	Urban	Long-term, modalities.
SVOX-night [ 16 ]	823/17166	Urban	Day/Night.
SVOX-overcast [ 16 ]	872/17166	Urban	Weather.

TABLE I: Overview of the test VPR datasets

Method	Image size	Nordland [ 23 ]			Tokyo24/7 [ 24 ]			MSLS-val [ 36 ]			AmsterTime [ 52 ]			Pitts30k-test [ 51 ]
Method	Image size	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10
SALAD [ 28 ] , CVPR2024	$224\times 224$	79.8	90.2	93.4	91.7	96.5	96.5	91.1	95.5	96.4	53.0	74.4	79.5	91.7	95.7	97.0
BoQ [ 21 ] , CVPR2024		83.9	92.4	95.0	94.3	97.5	97.8	90.9	95.7	96.4	53.6	72.5	76.9	93.0	96.4	97.3
EDTFormer [ 35 ] , TCSVT2025		80.9	91.2	94.2	96.5	97.8	98.1	91.9	96.1	96.6	58.9	80.3	84.7	92.8	96.8	97.8
ImAge [ 31 ] , NIPS2025		88.2	95.5	97.1	92.1	96.2	97.8	90.7	95.9	96.6	56.3	76.1	81.6	93.2	96.8	97.8
CerfeVPR [ 18 ] , CMC2025		92.5	96.8	97.8	\			87.7	94.2	95.3	\			94.9	97.5	98.0

TABLE II: R@N comparison to SOTA methods on benchmark datasets. The best results are in bold and the second best underlined .

Method	Nordland [ 23 ]						SVOX [ 16 ]
	Summer/Spring			Summer/Fall			Rain			Sun			Snow			Night			Overcast
	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10
SALAD [ 28 ]	96.2	98.3	98.9	99.4	99.7	99.7	98.6	99.7	99.8	97.3	99.1	99.5	98.6	99.5	99.7	95.5	99.3	99.4	98.4	99.3	99.3
BoQ [ 21 ]	97.3	99.1	99.4	99.5	99.8	99.8	98.5	99.6	99.7	97.4	99.4	99.4	99.3	99.8	99.8	97.2	98.9	99.4	98.3	99.2	99.4
EDTFormer [ 35 ]	97.3	98.9	99.4	99.6	99.8	99.8	98.3	99.8	99.8	98.4	99.5	99.8	98.5	99.7	99.8	95.7	98.8	99.5	98.1	99.2	99.4
ImAge [ 31 ]	97.6	99.2	99.5	99.4	99.7	99.8	98.0	99.6	99.6	97.4	99.4	99.5	98.7	99.5	99.7	97.3	99.1	99.6	97.6	99.1	99.2

TABLE III: R@N comparison to SOTA methods on the challenging benchmark datasets under varied domain conditions. Inference images are resized to

322\times 322

. The best results are in bold and the second best underlined .

$\lambda_{\text{adv}}^{q}$	$\lambda_{\text{adv}}^{\mathbf{x}}$	$\lambda_{\text{local}}$	Train set	MSLS-val	Nordland	Tokyo24/7
0	0	0	original	92.7	88.0	97.1
0	0	0	augmented	92.7	89.3	96.8
0	0.05	0		93.0	93.5	97.1
0.01	0.05	0		92.3	92.9	96.2
0.1	0.05	0		92.2	89.0	96.2
0.5	0.05	0		92.6	92.8	96.5

TABLE IV: Ablation on loss weights. ’Original’ and ’Augmented’ refer to the GSV-cities dataset used for training. The best R@1 results are in bold and the second best underlined .

$\alpha$	$G$	$H$	MSLS-val	Nordland	Tokyo24/7
0.05	10	4	92.8	93.3	97.1
0.05	10	8	93.6	93.5	97.5
0.05	10	12	93.0	94.7	96.5
0.05	10	16	92.4	92.9	96.8
0.05	10	20	93.1	92.8	97.1
0.05	10	24	93.1	91.0	96.5

TABLE V: Ablation on hyperparameters of the query-combination-based triplet supervision. The best R@1 results are in bold and the second best underlined .

Method	Dimension	MSLS-val	Nordland	Tokyo24/7	AmsterTime
BoQ [ 21 ]	12288	93.8	90.6	96.5	63.0
	4096	93.0	88.8	96.5	59.5
	2048	92.2	85.4	95.2	54.8
	512	85.8	68.4	88.6	34.9
QdaVPR(ours)	12288	93.6	94.1	97.1	64.0
	4096	93.6	93.5	97.5	61.1

TABLE VI: Ablation on descriptor dimensionality with the best R@1 results in bold .

Fig. 4: Average attention map visualization for (b) QdaVPR and (c) BoQ [ 21 ] . In the input images (a), green blocks indicate regions containing buildings. In (b) and (c), green blocks denote consistently high attention to the buildings shown in (a) across different weather conditions, whereas red blocks indicate high attention under only one specific weather condition and low attention under the other.

Visual Place Recognition

Visual Place Recognition matches camera observations to mapped places despite changes in viewpoint, lighting, weather, or time.

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Sep 3, 2026J/K move · Enter open · S save

AdaptVPR: Route-Aware Hard Positive Generation for Robust Visual Place Recognition

Shunpeng Chen, Jingyi Zhang, Changwei Wang, Shengpeng Xu+5

School of Artificial Intelligence, Beijing University of Posts and Telecommunications · Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology

Visual Place Recognition Synthetic Data Augmentation Domain Generalization

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Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby place, yet its robustness is often degraded by domain shifts arising from illumination, weather, seasonal changes, and dynamic occlusions. One contributing factor is the limited appearance diversity of the same place in existing training data. To address this issue, we propose AdaptVPR, a route-aware generative augmentation framework that constructs same-place hard positives for robust VPR training. AdaptVPR first uses a vision language model to parse scene attributes and estimate editing feasibility, while a rule-based scheduler determines the generation route according to editability scores and risk constraints. The generation process is decomposed into three complementary routes: the Global Appearance Route introduces global scene changes in weather, illumination, and time of day; the Local Occlusion Route inserts plausible dynamic occluders; and the Dual Route combines both types of perturbations to produce more challenging appearance shifts. Each generated candidate is evaluated using a VPR-oriented verification scheme based on geometric consistency and appearance diversity, reducing the risk of structural drift while ensuring sufficient appearance variation. Global candidates are generated once and rejected if verification fails, while Local Occlusion and Dual candidates use verification feedback for limited prompt refinement and regeneration. Using this framework, we construct AdaptCities, containing 160K verified synthetic same-place hard positives. Experiments across multiple VPR baselines and vision foundation backbones show consistent gains on standard benchmarks and substantial improvements under challenging domain shifts, with R@1 gains of up to 9.2%. The source code and data resources are publicly available at https://github.com/chenshunpeng/AdaptVPR.

Abstract

Visual Place Recognition

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