Title: MutualVPR: A Mutual Learning Framework for Resolving Supervision Inconsistencies via Adaptive Clustering
Venue: NeurIPS 2025
Accepted: yes
Keywords: Visual Place Recognition

Geo-localization，Autonomous Driving
Abstract: Visual Place Recognition (VPR) enables robust localization through image retrieval based on learned descriptors.
  However, drastic appearance variations of images at the same place caused by viewpoint changes can lead to inconsistent supervision signals, thereby degrading descriptor learning.
  Existing methods either rely on manually defined cropping rules or labeled data for view differentiation, but they suffer from two major limitations:
  (1) reliance on labels or handcrafted rules restricts generalization capability;
  (2) even within the same view direction, occlusions can introduce feature ambiguity.
  To address these issues, we propose MutualVPR, a mutual learning framework that integrates unsupervised view self-classification and descriptor learning.
  We first group images by geographic coordinates, then iteratively refine the clusters using K-means to dynamically assign place categories without manual labeling.
  Specifically, we adopt a DINOv2-based encoder to initialize the clustering.
  During training, the encoder and clustering co-evolve, progressively separating drastic appearance variations of the same place and enabling consistent supervision.
  Furthermore, we find that capturing fine-grained image differences at a place enhances robustness.
  Experiments demonstrate that MutualVPR achieves state-of-the-art (SOTA) performance across multiple datasets, validating the effectiveness of our framework in improving view direction generalization, occlusion robustness.

MutualVPR: A Mutual Learning Framework for Resolving Supervision Inconsistencies via Adaptive Clustering

Abstract

Visual Place Recognition (VPR) enables robust localization through image retrieval based on learned descriptors. However, drastic appearance variations of images at the same place caused by viewpoint changes can lead to inconsistent supervision signals, thereby degrading descriptor learning. Existing methods either rely on manually defined cropping rules or labeled data for view differentiation, but they suffer from two major limitations: (1) reliance on labels or handcrafted rules restricts generalization capability; (2) even within the same view direction, occlusions can introduce feature ambiguity. To address these issues, we propose MutualVPR, a mutual learning framework that integrates unsupervised view self-classification and descriptor learning. We first group images by geographic coordinates, then iteratively refine the clusters using K-means to dynamically assign place categories without orientation labels. Specifically, we adopt a DINOv2-based encoder to initialize the clustering. During training, the encoder and clustering co-evolve, progressively separating drastic appearance variations of the same place and enabling consistent supervision. Furthermore, we find that capturing fine-grained image differences at a place enhances robustness. Experiments demonstrate that MutualVPR achieves state-of-the-art (SOTA) performance across multiple datasets, validating the effectiveness of our framework in improving view direction generalization, occlusion robustness. The code can be found at https://github.com/Gucci233/MutualVPR .

1 Introduction

Visual Place Recognition (VPR) is the task of determining a previously visited location from a query image by matching it against a database of geo-tagged reference images. It serves as a key component in long-term localization and loop closure for autonomous systems such as mobile robots [10, 11, 32] and self-driving vehicles [12, 17].

Existing VPR methods either utilizes contrastive learning [4, 30, 20, 15, 2] or classification-based learning [26, 22, 6, 7] to learn the place representation. Contrastive learning-based approaches

Noviewdirectionclassification:

Eigenplaces

Ours

Geo-

Grids

：Viewdirection1

Placei

Viewdirection2

Viewdirection1

Placei

Viewdirection2

不

Placei

Place j

AdaptiveclusteringinEmbeddingspace

Viewdirection3

Viewdirection3

Viewpointvariations

Occlusions

Consistentsupervision

Inconsistentsupervision

facilitate the learning of robust and discriminative descriptors. However, they depend heavily on hard sample mining, which incurs significant computational cost and limits scalability to large-scale. Classification-based VPR methods divide the environment into spatial grids based on geographic coordinates, assigning each grid cell a unique class label, therefore, avoiding the need for expensive sample mining.

However, as shown in Figure 1 a), views captured from the same location but in different directions can result in drastically different visual scenes. If view direction is not considered, such views are treated as originating from the same place, which may introduce inconsistent supervision signals.

CosPlace [6] first attempts to address this problem by manually defining class labels based on view directions. However, this method does not provide explicit guarantees of intra-class visual similarity or inter-class visual distinctiveness. As a result, view variation may still persist. EigenPlaces [7] proposes a cropping strategy with the assumption that views toward a common reference point are similar. However, as shown in Figure 1 b), real-world scenes often involve occlusions from buildings, vehicles, and other structures. Such occlusions can lead to significant visual differences thus introduce inconsistent supervision. Consequently, visually dissimilar scenes may be incorrectly grouped under the same label. Such supervision inconsistency misaligns the supervisory signal with the true visual similarity, thereby undermining the model's ability to learn robust and discriminative features.

To address the problem of supervision inconsistency, we propose MutualVPR, a mutual learning framework that jointly refines image descriptors from the same geo-grids and view classification as shown in Figure 1 c). Unlike prior methods that rely on fixed or heuristically defined place labels, MutualVPR dynamically updates both feature representations and place label assignments through iterative, feature-driven mutual learning. This co-evolution enables the system to align semantic content with supervision more effectively.

Our main contributions can be summarized as follows:

We propose a mutual learning framework where feature learning and clustering co-evolve, effectively mitigating supervision inconsistency.

A adaptive clustering strategy dynamically refines pseudo-labels based on visual semantics, handling view directions and occlusions without orientation labels.

Our method achieves state-of-the-art (SOTA) performance on challenging VPR benchmarks, demonstrating robustness to diverse appearance variations and inconsistent supervision.

2 Related Works

2.1 Contrastive Learning-based VPR

Recent advances in VPR have largely benefited from deep learning-based approaches. Methods such as NetVLAD [4] pioneered the use of trainable aggregation layers to produce compact and discriminative global image descriptors. Contrastive learning [8, 24] has become prevalent, employing triplet-based or contrastive objectives to encourage the model to learn discriminative representations.

However, images taken at the same location may be captured from very different view directions-resulting in large visual appearance gaps between positive samples. Conversely, images from nearby but distinct locations may look similar due to aligned view directions, increasing the risk of erroneous negatives. This mismatch between place labels and visual similarity can mislead contrastive objectives and degrade performance.

To mitigate the impact of view direction variation, MixVPR [2], CricaVPR [20], SALAD [15] and BoQ [3] train on the GSV-Cities dataset [1], where all images within the same class share a consistent view direction. By ensuring this, these methods reduce inconsistent supervision.

Sample mining strategies such as GCL [19] and Clique Mining (CM) [14] further enhance learning: GCL assigns graded similarity labels to reduce supervision noise, while CM forms batches of very similar images to create harder training samples.

Despite these improvements, all these approaches still face challenges when images from the same location exhibit diverse view directions or occlusions, limiting their generalization under extreme conditions.

Other methods [9, 13, 33, 21] aim to enhance robustness by incorporating local feature matching, but they often rely on a two-stage process, which incurs significant computational overhead.

2.2 Classification-based VPR

Despite their [2, 20, 15, 3, 18] success, contrastive learning-based methods rely on hard sample mining, which increases training complexity and computational overhead. An alternative approach is to formulate VPR as a classification problem, reducing the need for explicit pairwise comparisons. Methods such as DaC [28] directly partitions images into grids based on their geographic coordinates, training the feature extractor by classifying images into their corresponding grids. However, it does not take into account the variations in viewpoint within each grid. Furthermore, CosPlace [6] and EigenPlaces [7] categorize images into location and view-based classes, allowing efficient training with categorical cross-entropy loss.

CosPlace[6] partitions the dataset into geographic cells and classifies images based on view direction labels. However, this rule-based scheme overlooks visual similarity among samples within the same cell. As a result, it often assigns semantically similar images to different class and vice versa, leading to inconsistent supervision.

EigenPlaces [7] introduces a classification scheme based on the Singular Value Decomposition (SVD) of image locations, grouping images that share a common reference point. A key advantage of this approach is its independence from manually defined view direction labels. However, the underlying assumption-that images oriented toward the same reference point share similar visual content-often fails in urban environments due to occlusions caused by buildings, vehicles, or vegetation. Consequently, such scenes may exhibit substantial visual differences despite similar viewing intent, resulting in supervision inconsistencies that undermine classification reliability.

3 Problem Analysis

To better understand the issue of supervision inconsistency in classification-based VPR methods, we visualize the image descriptors extracted by CosPlace [6], EigenPlace [7], and our method using t-SNE.

As shown in Figure 2, each View corresponds to an image captured at a specific geographic position and camera orientation, while a Class denotes a group of views considered to represent the same place.

In practice, class labels can be assigned based on orientation labels (as in CosPlace), where each direction corresponds to a predefined class. However, this scheme often fails to align with true scene semantics, leading to inconsistent supervision when visually similar views fall into different classes. Alternatively, labels derived from descriptor clustering group images by visual similarity, naturally ensuring semantic consistency.

Such supervision inconsistencies mainly manifest as view variations and occlusions. View variation can be further divided into two types:

CosPlace(orientationlabels)

EigenPlaces(orientationlabels)

class1

class2

View

OT

Variation

(e

View1

View II

-10

class4

class5

10

10

20

-20

10

10

20

View

Ours(orientationlabels)

Ours(clusteringlabels)

Variation

b):View I, View I1

b):View I, View II

b)

View1

ViewII

classi

a):ViewIl, ViewII

10

a):ViewIl,View II

Occlusion

-30

20

classo

10

class1

10

20

class2

30

class3

-30

20

.

class4

10

class5

10

20

30

Viewli

ViewIII

View variation a): Views with large scene overlap are assigned to different labels due to orientation labels based on view direction. In panel a), Views II and III are labeled as Class 2, while the highly similar View I is labeled as Class 1. As shown in the t-SNE plots of CosPlace and EigenPlaces, their features directly inherit this flawed supervision, forming two separate clusters despite the strong visual overlap. When our learned features are colored by orientation labels, this artificial split remains; however, coloring the same features by clustering results yields a single, coherent group. This contrast highlights the core issue - the conflict between fixed directional supervision and actual visual semantics.

View variation b): The opposite inconsistency occurs when visually distinct views are assigned the same label. In panel b), Views II and III share Class 5 despite clear visual differences, while View I (Class 4) is semantically closer to View II. Feature visualizations colored by orientation labels reflect this mistaken grouping, whereas clustering-based coloring reorganizes them into more meaningful, semantically consistent clusters. This again exposes the mismatch between visual reality and rigid label assignment, underscoring the need for adaptive supervision.

Occlusion-induced inconsistencies are demonstrated by 'Occlusion' in Figure 2, where images captured from different viewpoints but oriented toward the same reference point may exhibit significantly different visual content due to obstacles. In the "Occlusion" panel, Views I, II and III are captured from the same geo-grid but are occluded by trees or different buildings, leading to distinct visual content. Although these views share the same reference point, their semantic content diverges. This violates the assumptions of EigenPlace, resulting in supervision inconsistency.

We therefore argue that effective supervision for VPR should reflect semantic similarity rather than rely solely on spatial proximity or fixed directional assumptions. Without semantically consistent supervision, models are prone to learning unstable features, reducing their ability to generalize across complex, realworld scenarios.

4 Approach

FeatureExtractor

Mutual LearningviaAdaptive Clustering

UTM Grids

withPriorClassificaion

stage2

Feature

LMCL

CrossEntropy

Extractor

Classifier

Loss

DINOv2

GeM

Linear

3

stage1

iteration

Consistent

Supervision

Feature

装

装

Extractor

NOrB

MHA

No

M

装

UTMGrids

→

LP

C1

withPosteriorClassificaion

m

m

K-Means

C4

Update

?

C2

Trainable

Adapter

C4

2

Frozen

The proposed MutualVPR framework integrates unsupervised view self-classification with joint descriptor training in a mutual learning paradigm (as shown in Figure 3). The key lies in its adaptive clustering mechanism, which iteratively refines place categories throughout training.

4.1 Feature Encoder

MutualVPR is built upon DINOv2 [23], and incorporates the MulConv adapter [20] to enhance the model's capacity for robust feature representation. MulConv adopts a bottleneck structure with three parallel convolutional branches operating at different receptive fields, enabling the extraction of multi-scale features. This design improves the model's ability to handle variations in object scale and spatial context, which is beneficial in environments with diverse structural patterns. It can be expressed as:

where z l -1 denotes the input features from the previous layer, LN( · ) is Layer Normalization, and MHA( · ) refers to Multi-Head Attention. MLP( · ) stands for the feed-forward network, while Adapter( · ) represents the MulConv adapter module. The parameter s is a learnable scaling factor that controls the contribution of the adapter branch.

4.2 Mutual Learning via Adaptive Clustering

4.2.1 Place Label Initialization

Similar to CosPlace[6], but without using view direction labels, we first partition images into coarse location-based classes using UTM coordinates. At this stage, view direction variations within the same location are not considered. Formally, a coarse location class is defined as:

where M is a hyperparameter controlling the size of the grid. This step serves as an initialization of place labels, which are later refined through adaptive clustering.

4.2.2 Mutual Learning of Feature Extraction and Clustering

While UTM-based grouping ensures spatial locality, it fails to capture appearance variations caused by view direction changes and occlusions. To address this, we introduce a mutual learning framework that combines feature-aware supervision with adaptive clustering.

Within each coarse UTM region, we perform iterative K-means clustering based on learned descriptors, enabling view direction categories to adapt dynamically as feature representations evolve. Formally, view direction categories are defined as:

where K controls the granularity of view direction partitioning. Unlike static classification methods with fixed view direction labels, our approach allows continuous refinement of view direction clusters, better aligning them with actual visual similarity.

As training progresses, the feature encoder and clustering process update each other iteratively. This self-correcting mechanism avoids error accumulation from early misassignments and ensures images are grouped into more consistent view direction categories over time.

Following CosPlace [6], we adopt Large Margin Cosine Loss (LMCL) [29] as our classifier to enforce discriminative feature learning. At inference time, descriptors are directly extracted from the feature encoder and used for image retrieval based on their distances.

4.3 Implementation Details

In the implementation, DINOv2's ViT-B 14 is employed as the backbone network, with all parameters frozen except for the Adapter. Input image size is resized to 504×504 to meet the input requirements of ViT-B 14. GeM pooling and a fully connected layer reduce the dimensionality to 512 for the final descriptor. For dataset classification, margin M is set to 10, and K clusters are set to 3. The feature extractor is initialized with a learning rate of 1e-5, while the classifier uses 1e-2. We employ the Adam optimizer and apply a cosine annealing scheduler to the feature extractor. Training is conducted for 50 epochs, each consisting of 10,000 iterations. To reduce training time due to the huge dataset, we divide all UTM classes from the coarse classification into eight groups, with each group serving as the training set for one epoch. In each epoch, one-fifth of the classes within the selected group is randomly chosen for feature-aware clustering.

During training, we found that increasing overlapping ratio of cropped images can enhance semantic continuity. For the multi-angle cropping strategy, we crop panoramic images from different starting angles every 60° to generate training data. In our experiments, the starting angles are set to 0° and 30°. Since all images are cropped from panoramas, adjacent images naturally share semantic content. In our work, adjacent classes-such as class 2 and 3-represent neighboring view directions.

5 Experimental Results

5.1 Research Questions

In this work, we focus on the supervision inconsistency problem in VPR. We aim to investigate the following research questions:

Q1 : How does our method perform compared to SOTA VPR approaches across standard benchmarks?

Q2 : How well does our method generalize to challenging dataset with occlusion?

Q3 : How does our adaptive clustering compare to orientation labels in handling supervision inconsistency?

5.2 Datasets and Evaluation Metrics

For contrastive learning-based VPR methods, we use the GSV-Cities dataset [1] for training. For classification-based VPR methods, we use the SF-XL dataset [6] for training. The selected training set is a subset of about 0.9M panoramic images, following CosPlace. A multi-angle cropping strategy as describe in Section 4.3 is applied. Eigenplaces use all panoramic images of about 3.4M for cropping.

For evaluation, we adopt several widely used VPR benchmarks: Pitts30k-test, Pitts250k-test [4], MSLS-val [31], Tokyo 24/7 [27], and SF-XL-test v1 [6]. To assess the generalization capability of our method, we also evaluate it on SF-XL-occlusion [5] dataset. This dataset, originally designed to assess the robustness of VPR methods under severe occlusion, also exposes the limitations of conventional supervision strategies. The detailed statistics of the datasets can be seen in Appendix A.

We employ the standard Recall@K metric, defined as the ratio of correctly located queries to the total number of queries. Correct localization involves searching for positive examples by matching images within 25-meter radius threshold based on geographical coordinates.

The experiment is executed on a server with three NVIDIA RTX 3090 GPUs, using PyTorch for training and testing.

5.3 Comparison with Other Methods

To answer the research question Q1, we compare our method with SOTA VPR methods, including both contrastive learning-based and classification-based approaches. The former includes NetVLAD [4], GeM [25], AnyLoc [18], ConvAP [1],MixVPR [2], CricaVPR [20], BoQ [3], SALAD [15] and CM[14]. The latter includes EigenPlaces [7], and CosPlace [6].

It should be noted that our NetVLAD is trained on a ResNet-50 backbone, unlike the original version trained on VGG16. Nevertheless, its descriptor dimensionality is still very high, which prevents evaluation on the SF-XL-test set due to memory constraints when processing 2M samples. The comparison results are shown in Table 1. It shows that MutualVPR consistently delivers competitive or superior performance across multiple benchmarks, showing strong generalization capability.

For classification-based baselines, MutualVPR generally outperforms CosPlace across datasets, even when CosPlace uses ground-truth labels. This demonstrates the benefit of our adaptive clustering, which mitigates the limitations of orientation labels with fixed splits that may misrepresent visual similarity under viewpoint changes or occlusions. EigenPlaces, on the other hand, is specifically designed to handle extreme viewpoint variations. While it performs well on SF-XL-testv1, its performance drops on more diverse datasets such as Tokyo 24/7 and MSLS-Val. This is because EigenPlaces' strong focus on viewpoint invariance can limit its generalization to scenarios involving occlusions or other challenging conditions. In contrast, MutualVPR achieves a balance: it effectively handles viewpoint variations while remaining robust to occlusions and other extreme conditions, leading to better overall generalization. Supporting experiments validate this claim in Appendix D.

Among contrastive learning-based methods, those trained on the GSV-Cities dataset generally exhibit strong performance. This is largely attributed to the nature of GSV-Cities, which contains multiple captures of the same location under highly consistent viewpoints. Such data inherently mitigates supervision inconsistencies caused by view variations, allowing contrastive methods to learn more stable representations. However, achieving this level of performance requires large-scale, carefully

curated data and extensive sample mining. Recent state-of-the-art methods, such as SALAD+CM, further leverage large and diverse training sets (MSLS + GSV-Cities) to achieve the highest average performance across multiple benchmarks.

In contrast, our MutualVPR is trained solely on SF-XL yet achieves robust and well-balanced performance across diverse conditions. Although not always the top performer on every individual benchmark, it ranks closely behind SALAD+CM in overall accuracy and even surpasses it on Pitts30k and Tokyo24/7 in terms of R@5. Moreover, unlike methods such as CricaVPR ∗ , which rely on batch-level feature interaction to enhance localization accuracy and suffer a substantial drop in single-query inference (as indicated by CricaVPR 1 ), our approach maintains stable performance without requiring inter-sample dependencies, further underscoring its practical robustness.

5.4 Evaluate on Occlusion Dataset

To answer research questions Q2, we evaluate the generalization capability of our method using a dataset characterized by significant occlusions, which further reflects the impact of inconsistent supervision on retrieval performance. Table 2 presents the retrieval performance on the SF-XLOcclusion dataset, where each query is affected by occlusion.

Our method achieves 47.4% R@1, significantly outperforming all baselines, including EigenPlaces (36.8%) and CosPlace (32.9%), as well as several contrastive learning methods such as MixVPR (30.3%) and SALAD (31.6%). Among the contrastive learning baselines, CricaVPR 1 (40.8%), SALAD+CM (40.8%), and BoQ (38.2%) show competitive performance, but still remain below our method for top-k retrieval metrics. These results demonstrate that our approach maintains robust retrieval under heavy occlusion.

Our strong performance under occlusion stems from two key advantages:

Adaptive Supervision Consistency: Unlike static supervision methods (e.g. CosPlace, EigenPlaces), our approach refines class assignments through iterative, feature-aware clustering. This process corrects initial supervision errors caused by occlusions or view changes, progressively aligning features from occluded and unoccluded samples belonging to the same place. As a result, the model learns to associate semantically similar scenes across varying view directions, improving label consistency and robustness.

See Appendix B.1 for a visual example showing a misclassified occluded query being reassigned to the correct class after training.

Semantic Proximity within Class: Our method brings semantically similar views closer in feature space, even when they originate from different view directions. This contrasts with rigid label-based schemes, where visually similar scenes are separated due to orientation labels.

This semantic proximity benefits retrieval: even if an occluded query is not correctly classified, it can still be retrieved as long as its feature lies within the threshold distance.

We conducted experiments showing that our method achieves consistently lower inter-class distances than CosPlace and EigenPlaces across adjacent view directions. Quantitative analysis and distance comparisons are presented in Appendix B.2.

These findings suggest that by iteratively updating class assignments based on feature similarity, our approach naturally brings semantically similar scenes-despite view differences or occlusions-closer in the feature space. This results in more compact and coherent class boundaries.

In contrast, these methods which rely on rigid, manually defined labels often split visually similar scenes into separate classes, creating artificial gaps in the feature space. Our method mitigates such fragmentation, enabling smoother transitions between adjacent classes and enhancing retrieval robustness.

5.5 Ablations

5.5.1 Adaptive vs Static Label Supervision

To answer research question Q3, we compare manually assigned view direction labels with our adaptive clustering approach across various settings.

Fixed view direction Labels (CosPlace) : Using predefined view direction labels from geographic metadata.

Fixed view direction Labels (CosPlace) + Cropping: Applying multi-angle cropping while still relying on predefined view direction labels.

Adaptive Clustering (Ours): Using our adaptive clustering but training on raw images without multi-angle cropping.

Adaptive Clustering (Ours) + Cropping: Incorporating both self-adaptive clustering and multi-angle cropping.

We conducted experiments using both DINOv2 and ResNet50 as backbones. For each starting angle, we cropped the panoramic image into six evenly spaced views; e.g. with starting angles of 0° and 30°, this results in a total of 12 cropped images. In our method using ResNet50, we clustered all views into six classes ( K = 6 ), while for DINOv2, we followed the same procedure but set K = 3 .

While CosPlace sees clear gains from multi-angle cropping on SF-XL-testv1 (R@1 up to 81.1%), its performance on Tokyo24/7 (R@1 at 85.1%) reveals limited generalization, likely due to overfitting.

In contrast, MutualVPR achieves consistently strong results across both datasets. Both methods benefit from the multi-angle cropping strategy, which increases semantic continuity, but MutualVPR gains more thanks to its adaptive clustering that better aligns semantically similar views.

5.5.2 Cluster Number

The number of clusters, K , is a hyperparameter that determines the granularity of dataset partitioning using K-means. With fewer clusters, the images within each class tend to be more compact and exhibit higher similarity in the feature space. Conversely, a larger K results in finer partitioning, increasing the diversity of images within the feature space. In our experiments, we evaluated K = 1 , 3 , 6 , and the corresponding results are shown in Table 4.

As expected, the case without clustering ( K = 1 ) yields the worst performance, demonstrating the effectiveness of incorporating view direction classification. For the ResNet50 backbone, performance is highest when K = 6 , though the improvement over K = 3 is marginal. In contrast, for the DINOv2 backbone, optimal performance is achieved with K = 3 , outperforming K = 6 by approximately 10% . These results suggest that the optimal number of clusters depends not only on the dataset but also on the backbone. Determining an appropriate K for a given dataset and model remains an open question in the context of the proposed method.

More ablation studies can be found in the Appendix C.

6 Conclusion and Future Work

We proposed MutualVPR, a mutual learning framework that addresses supervision inconsistencies in VPR caused by view direction variations and occlusions. By dynamically refining view direction categories through adaptive clustering guided by feature learning, our method eliminates reliance on orientation labels and achieves semantically consistent supervision. Extensive experiments show that MutualVPR achieves robust and generalizable performance across diverse and challenging datasets, validating the effectiveness of our adaptive clustering strategy for real-world VPR applications.

A limitation of our approach is the fixed cluster number K , which may not fully capture varying view direction distributions. Since classification and descriptor learning are mutually reinforced, a static K may limit model's adaptability. Our studies underscore its impact on performance, suggesting the need for dynamic adjustment. Future work will explore adaptive clustering to optimize K based on dataset characteristics, enhancing the synergy between classification and representation learning.

Acknowledgments and Disclosure of Funding

This work is supported by the National Key Research and Development Program of China (No. 2020YFA0711402).

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A Dataset Details

We train on several large-scale datasets, including SF-XL[6] for classification-based methods , GSVCities [1] for contrastive learning, and evaluate on Pitts30k-test [4], MSLS-val [31], Tokyo 24/7 [27].

GSV-Cities is a large-scale dataset containing 560k images depicting 67k unique places captured from consistent viewpoints, each labeled with geographic coordinates. SF-XL consists of images cropped from panoramic views at the same location, covering diverse viewing angles and acquisition years, making it suitable for learning viewpoint- and time-robust representations.

A summary of above datasets is provided in Table 5.

B Supervision Correction and Feature Distance Analysis

To further support our claims on supervision correction and semantic proximity, we provide additional visualizations and feature-level analysis of our method.

B.1 Visualization of Supervision Correction

To demonstrate how adaptive clustering resolves initial supervision inconsistencies (e.g., caused by occlusion), we visualize clustering results before and after training.

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As shown in Figure 4, we select a query (black dot) and its neighboring samples from the same geo-grid cell. Initially, the query is assigned to class 1, but after training, it transitions to class 2. These two classes correspond to adjacent view directions with overlapping semantic content.

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Figure 5 shows a visual comparison of samples in class 1 and class 2, where class 2 clearly exhibits stronger visual similarity with the query. This supports our claim that adaptive clustering can realign mislabeled samples by leveraging feature similarity during training, improving robustness to occlusion and supervision noise.

This demonstrates that our adaptive clustering effectively mitigates the impact of occlusions during training by refining feature-grouping over time. It enables the model to build robust associations between partially occluded and unobstructed views from the same location.

B.2 Feature Distance Analysis Across Classes

We further analyze how our adaptive clustering improves semantic continuity between classes by comparing feature distances of samples from adjacent categories.

To ensure fairness, we use SOTA method EigenPlaces as a proxy for selecting image pairs with adjacent labels and minimum mutual distances. As shown in Figure 6, we compare the pairwise feature distances obtained by our method and CosPlace.

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The results show that our method consistently achieves the smallest feature distances across adjacent view directions Even when we focus on sample pairs with closer feature distances (< 0.8), as shown in Figure 6 b. Our method still maintains closer feature relationships.

Our method consistently yields lower feature distances than CosPlace and EigenPlaces, indicating that our approach encourages more compact and semantically smooth transitions between neighboring classes. This explains why even occluded queries, if misclassified, can still retrieve the correct place as long as the distance remains within the retrieval threshold.

C More Studies

C.1 Comparison of High-Dimensional Descriptor

Since our method already achieves excellent performance with a relatively low descriptor dimension (512), we further conduct a fair comparison with SOTA methods under similar dimensional settings, as shown in Table 6. The original BoQ model has a very high dimensionality of 12288, making it infeasible to evaluate on the SF-XL dataset. Therefore, we reduce its projection dimension from 384 to 256, resulting in a total descriptor dimension of 8192.

To ensure fairness, we trained BoQ, SALAD, and SALAD+CM for 30 epochs on the GSV-Cities dataset until convergence. As shown in Table 6, most methods benefit significantly from higher feature dimensions. In contrast, our method shows only a moderate improvement when increasing the dimension from low (Table 1 and Table 2) to high, possibly because our dimensionality control is achieved through a simple MLP, whereas other methods involve additional internal projection layers.

Nevertheless, our method still delivers outstanding performance, achieving the best results in both R@1 and R@5 metrics.

C.2 Fine-tuning Strategies

To assess the effectiveness of our framework in accommodating different fine-tuning strategies, we evaluated MulConV, the method used in our work for adapting DINOv2 features, against PEFTbased approaches proposed in SelaVPR [21] and EDTformer [16]. Table 7 presents retrieval performance across multiple datasets, including Tokyo 24/7, MSLS-Val, Pitts30k, SF-XL-v1, and SF-XL-Occlusion.

Table 7: Comparison of different fine-tuning strategies within our framework. Metrics are R@1 / R@5.

The results demonstrate that all fine-tuning strategies achieve competitive performance, indicating that our framework effectively leverages pretrained representations. Among the evaluated methods,

MulConV consistently delivers the highest retrieval accuracy across all datasets, particularly under challenging conditions with occlusions or extreme viewpoint variations. This superior performance motivated our choice of MulConV as the fine-tuning strategy in our framework. These findings highlight both the flexibility and robustness of our approach, showing that it can accommodate various adaptation methods while benefiting most from MulConV.

C.3 Comparison of Different Backbones

To investigate the impact of different backbone architectures on retrieval performance, we conduct experiments using VGG16, ResNet50, and DINOv2 as feature extractors under a consistent training protocol (with K = 3 and no differential cropping). The results are shown in Table 8.

Despite having significantly fewer FLOPs than VGG16, ResNet50 achieves much better performance (R@1 of 73.9 vs. 61.5), highlighting the advantage of deeper residual connections and stronger feature representations. DINOv2, a vision transformer pretrained with self-supervised learning, achieves the best retrieval accuracy (R@1 of 77.0 and R@5 of 84.6), even though only a small adapter is trained on top. This confirms the strong generalization and representational capacity of DINOv2 features, making it a suitable backbone for downstream place recognition tasks.

These findings support our choice of using DINOv2 in the main experiments, striking a good balance between high performance and efficient finetuning.

D Discussion on EigenPlaces' Performance on SF-XL-testv1

Although our method generally outperforms existing approaches, it shows a slight drop on SF-XLtestv1, where EigenPlaces performs marginally better.

This arises from their methodological difference: EigenPlaces, being geometry-driven, groups views of the same focal point from different directions, thus enforcing a strong viewpoint-invariant prior that aligns well with SF-XL's panorama-cropped, multi-view structure.

In contrast, our approach clusters images purely in visual feature space. While it lacks explicit geometric constraints, it learns semantically and spatially coherent clusters that generalize more flexibly to complex scenes with occlusion, clutter, or fine-grained variations.

To verify the above observation and better understand the performance gap on SF-XL-testv1, we conducted a quantitative analysis to measure the degree of viewpoint invariance across different class construction strategies. Specifically, we sampled approximately 9k UTM grids (about 1M images) and applied k-means clustering using three methods: CosPlace, EigenPlaces, and ours. For each grid, we computed the feature distances between clusters with opposing headings (0° vs. 180°), as illustrated in Fig. 7.

The average inter-cluster distances were CosPlace: 1.1614, EigenPlaces: 1.0759, and Ours: 1.1247. These results quantitatively confirm that EigenPlaces exhibits the strongest viewpoint invariance, which explains its superior performance on benchmarks characterized by extreme viewpoint changes such as SF-XL-testv1. Conversely, CosPlace shows the largest inter-cluster distance and correspondingly lower performance, further validating that this metric meaningfully reflects a model's sensitivity

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces mutualVPR, a method designed to address label inconsistency in classification-based Visual Place Recognition (VPR) techniques, which can degrade performance and generalization. The authors identify three main causes for label inconsistency: \n\n1. Overlapping visually similar scenes being assigned different classes due to differing view directions.\n\n2. Visually distinct scenes captured from similar directions being assigned the same class.\n\n3. Views from the same reference point exhibiting varying semantic content due to occlusions.\n\nmutualVPR aims to resolve these inconsistencies by jointly learning classification-based VPR while iteratively refining initial geo-based labels. This self-supervised refinement enforces semantic consistency of learned features through K-means clustering of visual features, which is iteratively applied as the features evolve during the learning process. The method is evaluated on general and occlusion-specific VPR benchmarks and compared to several recent baselines. The quality of the proposed clustering method is further compared to manual labelling techniques.\n\nSTRENGTHS AND WEAKNESSES:\n*Strengths*\n\n1. The paper identifies and tackles a significant limitation in the current labeling and supervision strategies for classification-based Visual Place Recognition (VPR) methods. The proposed VPR method effectively combines adaptive clustering with feature learning to mitigate label inconsistency.\n2. The method demonstrates competitive performance against recent state-of-the-art classification-based and contrastive-based VPR techniques across multiple standard benchmarks. It also achieves state-of-the-art results on benchmarks specifically designed for VPR in occluded environments.\n3. A comparative analysis reveals that the shows clustering-based adaptive labeling strategy outperforms manual labeling strategies used by other state-of-the-art VPR \n4. The paper is well-structured and clearly written.\n\n*Weaknesses*\n\n1. The effectiveness of the proposed labeling method appears to be intrinsically linked to the quality of the learned features from the VPR (Visual Place Recognition) model it's coupled with. However, the paper does not compare the performance of their method across different VPR architectures, which makes it difficult to fully appreciate the specific contribution of their proposed approach.\nMoreover, a key ambiguity also remains regarding whether the authors introduce a new neural network architecture in addition to their adaptive clustering method:\n- If a new architecture is proposed, the absence of an ablation study comparing its performance with and without adaptive clustering limits the reader's ability to isolate the contribution of the backbone from that of the adaptive labeling.\n\n- Conversely, if no new architecture is proposed, the specific base architecture used is not explicitly mentioned. In this scenario, it would be important to evaluate the proposed labeling method when coupled with multiple VPR baselines to demonstrate its generalizability.\n2. Static K Hyperparameter: The method relies on a static K hyperparameter, which significantly influences performance (within the margin considered for outperforming other methods in the comparative analysis). The paper does not address how to estimate the optimal or expected impact of K, although the authors do acknowledge this as a limitation.\n3. Inconsistent Performance Discussion: The paper provides limited discussion regarding instances where classification-based methods outperform the proposed method on the SF-XL dataset, both in the comparative analysis and when analyzing the effect of the labeling method. A deeper dive into these specific cases would strengthen the analysis.\n4. The proposed method is limited to classification-based VPR methods (or at least presented as such)\n\nQUESTIONS:\n*Questions*\n1. Have you directly applied your adaptive clustering method with existing VPR models, such as CosPlace, to enable a more direct and fair comparison?\n\n2. Have you explored applying continual learning to classification-based VPR, where initial labels are refined with your adaptive approach, rather than training from scratch?\n\n*Suggestions*\n1. Please clarify whether the VPR architecture used is an existing one or a novel contribution. If it's a new architecture, consider adding an ablation study to assess its specific contribution relative to the adaptive labeling strategy. Otherwise, it is important to evaluate it across multiple baselines.\n\n2. Extending the discussion on the method's inferior performance on the SF-XL dataset, both in the comparative analysis and the context of the label analysis. This would help readers understand the specific scenarios where this approach may be less effective.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors identify and visualize an interesting problem in existing classification-based Visual Place Recognition (VPR) methods: inconsistent supervision caused by significant viewpoint variations and occlusions. This paper proposes a mutual learning framework that integrates unsupervised K-means clustering with feature descriptor training. Rather than relying on fixed or heuristically defined place labels, the proposed method defines and refines place labels through adaptive clustering in the embedding space. A MulConv adapter is incorporated into the frozen DINOv2 backbone to extract multi-scale features. In the mutual learning loop, clusters are updated by K-means while the extractor achieves consistent supervision by the posterior classification. Comparative experiments of various methods on popular benchmarks demonstrate excellent performance, especially on the dataset with severe occlusions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The overall writing is satisfied, the authors clearly illustrate the limitations of existing classification-based visual place recognition baselines, and then propose their framework to address these issues with convincing qualitative and quantitative results.\n2. This paper focuses on an interesting yet important issue of existing classification-based VPR methods, *i.e.*, the supervision inconsistency due to their fixed or heuristically defined place labels, and proposes a novel mutual learning framework that dynamically updates both feature representations and place label assignments.\n3. The experiments look good, demonstrating the superiority of the proposed method by comparison with both contrastive learning-based and classification-based baselines.\n\nWeaknesses:\nI haven't seen any major weakness, some minor issues are listed as follow:\n1. Compared to some recent baselines in this paper such as CricaVPR and BoQ, the number of baselines for comparison with the proposed method is less, hence it is suggested to include more baselines to present a comprehensive comparison, such as GeM [1], SFRS [2], Conv-AP [3], GCL [4]. \n2. The BoQ baseline used 4096 and 16384 dimensional descriptors for comparison in their paper, so it is strange why this paper re-trained BoQ with only 512 dimensional descriptors for comparison while other baselines (MixVPR, CricaVPR) use 4096 dimensional descriptors. And it is suggested to evaluate the proposed MutualVPR with diversified dimensional descriptors to demonstrate its effectiveness.\n3. The authors omit the reference of GeM [1] in this paper, which is used as a part in the proposed framework.\n4. The introduction of the modules in the proposed framework is too brief, such as the GeM pooling and the LMCL classifier, more description and explanation of why they are suitable for this framework are beneficial.\n5. The colors of different classes in the left and the right panels of Figure 2 are not consistent.\n6. The authors omit the information of training hyper-parameters, such as the learning rate, the learning scheduler, and the optimizer.\n\n[1] Filip Radenovi´c,  et al. Fine-tuning CNN image retrieval with no human annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2018.\n\n[2] Yixiao Ge, et al. Self-supervising fine-grained region similarities for large-scale image localization. European conference\non computer vision (ECCV), 2020.\n\n[3] Amar Ali-bey, et al. GSV-CITIES: Toward appropriate supervised visual place recognition. Neurocomputing, 2022.\n\n[4] Mar´ıa Leyva-Vallina, et al. Data-efficient large scale place recognition with graded similarity supervision. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.\n\nQUESTIONS:\nPlease refer to weaknesses.\n\nLIMITATIONS:\nYes, limitations have been included as a part of the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel training protocol for VPR. Existing works that adopt a classification framework rely on UTM coordinates for assigning samples to classes, which is prone to perceptual aliases. The paper introduces an unsupervised strategy based on feature clustering to adaptively divide into classes samples within a given geographical area. The architecture entails a frozen Dinov2 backbone with parameter-efficient tuning\n\nSTRENGTHS AND WEAKNESSES:\nStrenghts\n- the proposed training protocol removes the need for heading labels, which can be hard to obtain in many practical scenarios\n- Compared to recent state of the art (which are not cited btw, see weaknesses), which finetune DINO and use large output descriptors, the proposed method keeps DINO frozen, trains only lightweight adapters, and keeps dimensionality compact.\n- the results on SF-XL occlusion, and the comparison with manual heading supervision are interesting and show the value of the proposed algorithm\n- the proposed method improves upon Eigenplaces with a solid framework, removing the need for a lateral and frontal loss. \n\nWeaknesses:\n- literature study: the literature review is missing several relevant works. [1] is relevant for classification based frameworks, it also avoids using heading labels. [2] is relevant for view-dependent sample mining. [3] is the missing citation for SF-XL occlusion dataset. [5, 6] represent the state of the art in VPR and are not compared. Although they use much larger descriptors should be considered. [4] uses parameter-efficient tuning on top of DINO\n- writing clarity: throughout the paper a more consistent notation could be used. For example, when 'classes X and. Y' are mentioned by number, it is hard for a non-expert in the task to grasp the referred concept. Also, the related works section feels misplaced in the end of the manuscript. Section 2, \"problem analysis', could also benefits from more formal writing. I could understand the content given that I worked extensively on the task, but to make the paper more accessible some things should be made clearer, e.g. 'manual labelling' is misleading, it should be made clear that it means using heading labels with fixed splits. Same goes for the notation 'VIew X' and 'Class X' \n\n[1] Trivigno, Gabriele, et al. \"Divide&classify: Fine-grained classification for city-wide visual geo-localization.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[2] Leyva-Vallina, María, Nicola Strisciuglio, and Nicolai Petkov. \"Data-efficient large scale place recognition with graded similarity supervision.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n[3] Barbarani, Giovanni, et al. \"Are local features all you need for cross-domain visual place recognition?.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n[4] Lu, Feng, et al. \"Towards seamless adaptation of pre-trained models for visual place recognition.\" arXiv preprint arXiv:2402.14505 (2024).\n\n[5] Izquierdo, Sergio, and Javier Civera. \"Optimal transport aggregation for visual place recognition.\" Proceedings of the ieee/cvf conference on computer vision and pattern recognition. 2024.\n\n[6] Izquierdo, Sergio, and Javier Civera. \"Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.\n\nQUESTIONS:\n- in the experimental section it is not clear whether the proposed framework is compared with EigenPlaces and CosPlace with the original ResNet50, or with Dinov2 as the proposed method. the backbone should be the same for fair comparison\n- an ablation to study the effect of finetuning DINO vs PEFT should be included \n- at training time, why is a frozen feature extractor employed for clustering? this would double the amount of forward passes required. it is also common in contrastive netvlad-style training to use the same feature encoder updated as training procedes. is there any reasoning or experiment behind this choice?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

comments: SUMMARY:
MutualVPR is proposed to deal with inconsistent supervision signals used in current VPR methods. Various view directions and occlusions lead to unstable visual features and inconsistency. This approach is proposed to adaptively refine the image representations according to geometric grids clustered automatically, rather than manual labeling. Accurate and robust SOTA experimental results are shown.

STRENGTHS AND WEAKNESSES:
Strengths:
1. A novel labelling method to align the supervision with their geo locations. It simultaneously learns the clustering and the visual representations. Intra-grid classification shows a huge impact. 
2. This method shows good improvement in the VPR task on the testing datasets, especially the one with occlusions. 
3. The paper is well-written and easy to read. 

Weakness:
1. Missing comparison with other DINOv2-based VPR methods, such as AnyLoc and SALAD.
2. worse than EigenPlaces on SF-XL test v1.
3. As mentioned in the paper, the number of K is still an open question.

QUESTIONS:
1. How does the proposed labelling strategy facilitate the VPR task using the recall@k metric w.r.t. UTM coordinates? Relations between the geo-grid labelling and the commonly used test labels.
2. In Table 1, the descriptor dimensionality of NetVLAD is incorrect. Also, I assume the NetVLAD descriptors can be precomputed offline and compared on SF-XL-testv1. Pitts250k-test is recommended to be included in the tests.
3. Question about the choice of the number of clusters. For all experiments in the paper, do you always validate them separately for all datasets? How sensitive is the number of clusters for the training? and how different the optimal K is among different datasets.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29