MutualVPR: A Mutual Learning Framework for Resolving Supervision Inconsistencies via Adaptive Clustering
Authors: Qiwen Gu
Xufei Wang
Junqiao Zhao
Siyue Tao
Tiantian Feng
Ziqiao Wang
Guang Chen
Venue: NeurIPS 2025 poster ()
Keywords: Visual Place Recognition
Geo-localization，Autonomous Driving
Primary area: applications
TL;DR: A work on solving the problem of inconsistent supervision in visual place recognition

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
Qiwen Gu
Xufei Wang Junqiao 2
Siyue Tao Ziqiao Wang* Guang Chen
Tiantian Feng*
Sccooo of Computer Science and Technology. Tongji University, Shanghai, China
MOE Key Lab of Embedded System and Service Computing, Tongji University, Shanghai, China 3 SSannghai Research Institute for Intelligent Autonomous System, Tongji University, Shanghai, China "School of Surveying and Geo-Informatics, Tongji University, Shanghai, China
Shhanghai Innovation Institute
(2432178, tjwangxufei, zhaojunqiao, 2331923, annggiinniannntaaon edu. (ziqiaowang, guangchen) itongji. edu. cn
Abstract
Visual Place Recognition (VPR) enables robust localization through image re- trieval 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 manually defined cropping rules or labeled data for view differ- entiation, 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, mutual learning framework that integrates unsuper- vised view self-classification and descriptor learning. We first group images by geographic coordinates, then iteratively refine the clusters using K-means to dy- namically assign place categories without orientation labels. Specifically, we adopt 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, find that capturing fine-grained image differences at a place enhances robustness. Experi- ments demonstrate that MutualVPR achieves state-of-the-a (SOTA) performance across multiple datasets, validating the effectiveness of our framework in improv- ing view direction generalization, occlusion robustness. The code can be found https:: gi thub. www/Cucciiiaccccccm com//uucc1133/MutuaaaW
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 geo-tagged reference images. serves as key component in long-term localization and loop closure for autonomous systems such as mobile robots [10] 32
and self-driving vehicles
or classification. bassee learning 266 22 6 7 to learn the place representation. Contrastive learning based approaches
Existing VPR methods either utilizes contrastive learning 4
30 20 15
"Correspondngg author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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No view direction classification
Eigenplaces
Ours
Grids
View direction
Place
View direction
View direction
View direction 2
Place
Place j
Adaptive clustering Embedding space
direction
direction
Viewpoint variations
Occlusions
Consistent supervision
Inconsistent supervision
Figure 1: The problem of inconsistent supervision in existing VPR researches. The proposed mutual-learning framework define place labels by adaptive clustering embedding space, enforcing supervision consistency.
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-banl based VPR methods divide the environment into spatial grids based on geographic coordinates, assigning each grid cell unique class label, therefore, avoiding the need for expensive sample mining.
However, as shown in Figure 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 cropping strategy with the assumption that views toward common reference point similar. However, as shown in Figure bb), 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 visual similarity, thereby undermining the model's 's ability to learn robust and discriminative features.
To address the problem of supervision inconsistency, framework that jointly refines image descriptors from the same geo-grids and view classification as shown in Figure C)). Unlike prior methods that rely fixed 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.
propose Mutual mutual learning
Our main contributions be summarized as follows:
We propose mutual learning framework where feature learning and clustering co-evolve, effectively mitigating supervision inconsistency.
A adaptive clustering strategy dynamically refines pseudo-labed 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.

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2 Related Works
2.1
Contrastive Learning-based VPR
Recent advances in VPR have largely benefited from deep learning-base 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 contrastive objectives to encourage the model to learn discriminative representations.
However, images taken the same location may be captured from very different view direc- tions-ree riousineuu 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 negatíves. This mismatch between place labels and visual similarity can mislead contrastive objectives and degrade performance.
To mitigate the impact of view direction variation, Mix (2). CricaVPR [20]. SALAD [5] and BoQ [3] train on the GSV-Cities dataset M where all images within the same class share consistent view direction. By ensuring this, these methods reduce inconsistent supervision.
Sample mining strategies such GCL [99 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 create harder training samples.
Despite these improvements, all these approaches still face challenges when images from the same location exhibit diverse view directions occlusions, limiting their generalization under extreme conditions.
Other methods 9
3 33 21 aim to enhance robustness by incorporating local feature matching, but they often rely on two-stage process, which incurs significant computational overhead.
2.2 Classficatton-baseed VPR
Despite their 2 20 15 3 18 success, contrastive learning-basee 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 occlusions caused by buildings, vehicles, or vegetation. Consequently, such scenes may exhibit substantial visual differences despite similar viewing intent, resulting supervision inconsistencies that undermine classification reliability.
3 Problem Analysis
To better understand the issue supervision inconsistency in classification 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 image captured at a specific geographic position and camera orientation, while Class denotes a group views considered represent the same place.
3

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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 (orientation labels)
EigenPlaces (orientation
class
View Variation
class
dass S
View
Ours (orientation labels)
(clustering
Ninioe New
Méllew Liew
b)
Jonenn View
Alsfewn &L Meari
&
Occlusion
classo
class1
class2
class3
class4
class5
Views
View
Viens
Figure 2: Supervision Inconsistency in Classification based Methods. The left panel shows t-SNE visualizations of image descriptors extracted from single geo-grid using different methods the SF- XL dataset. "orientation labels" indicate samples colored according to their assigned view directions, while "clustering labels" refers to labels obtained by applying K-means clustering the descriptors. The top-right panel illustrates issues related to view variation, using image examples drawn from samples in the t-SNE visualization the left. The bottom-right panel illustrates occlusion induced issues, using image example from the same reference point.
Views with large scene overlap are assigned to different labels due to orientation labels based view direction. In panel a). Views II and III are labeled 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 single, coherent group. This contrast highlights the core issue the conflict between fixed directional supervision and actual visual semantics.
View variation a):
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.
deelusion-inducee inconsistencies are demonstrated by "Occlusion" in Figure 2 where images cap- tured 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 different buildings, leading to distinct visual content. Although these views share the same reference point, their semantic content diverges. This violates the assumptions of Eigen 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.

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Approach
Feature Extractor
Mutual Learning via Adaptive Clustering
stage2
Fenture Extractor
LMCL Classifier
CrossEntropy
GeM
Uiner
stage1
iteration
Consistent Supervision
Feature Extractor
UTN Grids
Posterior clavdificain
K-Mleam
Update
MulConV
Trainable
Froson
Figure 3: Mutual Learning Framework via Adaptive Clustering. We initialize spatial grids using UTM coordinates and assign coarse intra-grid categories. Features are extracted using DINOv2 with adapter and GeM [25] pooling, while adaptive clustering where iterative K-means guided by LMCL loss dynamically refines view direction categories within grids. Clusters and features co- evolve: updated clusters supervise feature learning (stage I), and improved features guide reclustering (stage 2), enabling robust supervision under occlusions and view direction changes.
The proposed Mutual framework integrates unsupervised view self-classification with joint descriptor training in mutual learning paradigm (as shown 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 's capacity for robust feature representation. MulConv adopts 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:
MHA (LN (=1-1)) 21-1;
MLP (LN (2f)) + Adapter (LN (z()) + 2/-
where 2t-1 denotes the input features from the previous layer, LN(.) Layer Normalization, and MHA(.) refers to Multi-Head Attention. MLP(-)) stands for the feed-forward network, while Adapter(-) represents the MulCony adapter module. The parameter s 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, coarse location class is defined as:
north
c/
where M a hyperparameter controlling the size of the grid. This step serves as an initialization of place labels, which are later refined through adaptive clustering.
5

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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:
C' Re, C,, R2 E Ex, h K},
where 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], 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 504x504 to meet the input requirements of ViT-B 14. GeM pooling and 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 learning rate of le-5, while the classifier uses le-2. We employ the Adam optimizer and apply cosine annealing scheduler 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-ange 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- as class 2 and 3 reperssent neighboring view directions.
Experimental Results
5.1 Research Questions
In this work, we focus on the supervision inconsistency problem in VPR. We aim investigate the following research questions:
Q1: How does 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 inconsis- tency?
5.2 Datasets and Evaluation Metrics
For contrastive learning-based VPR methods, we use the GSV-Cities dataset [ for training. For classification-basse VPR methods, we use the SF-XI. dataset (6] for training. The selected training set a subset of about 0.9M panoramic images, following CosPlace. multi-angle cropping strategy as describe in Section 4. is applied. Eigenplaces use all panoramic images of about 3.4M for cropping.

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For evaluation, we adopt several widely used VPR benchmarks: Pitts30k-test, Pitts250k-test [
MSLS-val [T], Tokyo 24/7 [27] and SF-XL-test vl 6 To assess the generalization capability of our method, we also evaluate it on SF-XI-occlusion S 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 metric, defined the ratio of correctly located queries to the total number of queries. Correct localization involves searching for positive examples by matching images within 25 2e-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
MSLS-val
Pitts30k
Pitts250k
Tokyo24/7
SF-XI-testvl
Method
Desc.dim.
Truin set
R@1
R@5
R@1
R@5
R@1
R@5
R@
R@5
R@1
R@5
NeIVAA
131072
GSV-Cities GSV-Cities
82.8
90.3
87.0
94.3
89.1
94.8
69.5
82.5
2048
72.5
82.7
84.5
85.1
93.4
73.7
25.6
35.5
AnyLoc VVT--B+GG))))
768
32.6
41.6
77.7
88.9
79.3
89.5
71.7
87.6
33.3
45.2
ConVAP
2048
GSV-Cities
81.5
87.5
89.7
95.2
91.2
96.4
74.6
83.2
41.1
53.0
MiX
4096
GSV-Ciries
87.1
91.4
91.6
95.5
94.3
98.1
87.0
93.3
69.2
77.4
CricaVPR'
4096
GSV-Cities
90.0
95.4
94.9
97.3
93.0
97.5
CricaVPR
4096
GSV-Cities
88.5
91.6
95.7
94.3
98.6
94.6
72.8
Cri (PCA)
512
GSV-Cities
87.1
92.6
90.4
94.9
92.5
97.1
87.4
92.9
68.4
77.1
BoQu
512
GSV-Citics
88.4
93.1
96.1
93.8
97.5
95.5
79.6
SALAD"
512+32
GSV-Cities
88.5
94.2
90.6
95.
92.1
97.0
92.3
95.1
70.2
77.7
SALAD+CM'
512432
MSLS+GSV-Cinies
90.4
96.2
90.9
95.9
93.2
97.8
92.8
96.2
78.4
EigenPlaces
512
SF-XI.
88.1
92.9
92.3
96.
93.5
97.5
84.8
94.0
83.8
89.6
CosPlace
512
90.2
89.6
90.4
96.6
89.2
64.8
MutualVPR (Ours)
512
SF-XL
89.2
95.1
90.9
96.4
92.6
97.9
92.4
96.6
80.8
86.4
Table 1: Comparison of various methods on multiple benchmark datasets. The upper block lists contrastive learning methods, while the lower block lists classification-base based methods. Crica denotes the results from the original paper relying on batch interaction, and Crica VPR, is our single- query variant. indicates retrained 512-D or 512+32-D version. Best results are shown in bold, second best are underlined.
To answer the research question Q1, we compare our method with SOTA VPR methods, including both contrastive barming-based and classification-busee approaches. The former includes NetVLAD
4]. GeM (251, AnyLoc [88. ConvAP Mix (2), CricaVPR [20], BoQ 3. SALAD [3] and CM [14] The latter includes EigenPlaces and CosPlace (6)
should be noted that our NerVLAD 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 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-trute 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, specifically designed to handle extreme viewpoint variations. While it performs well on SF-XI-testv| 1, 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 balance: 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 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
7

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curated data and extensive sample mining. Recent state tfate-of-thh-anne 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, MutualVPR is trained solely SF-XI. 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 and Tokyo24/7 in terms of @@5. Moreover, unlike methods such as CricaVPR*, which rely on batch-level feature interaction enhance localization accuracy and suffer substantial drop in single-query inference (as indicated by CricaVPR, our approach maintains stable performance without requiring inter-sample dependencies, further underscoring its practical robustness.
Pitts3kk
5.4 Evaluate on Occlusion Dataset
To answer research questions Q2, we evaluate the generalization capability of our method using 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-XL- Occlusion dataset, where each query affected by occlusion.
Our method achieves 47.4% R@ 1, significantly outperforming all baselines, including EigenPlaces (36.8%) and CosPlace (32.9%), well several contrastive learning methods such Mix VPR (30.3%) and SALAD (31.6%) Among the contrastive learning baselines, CricaVPR, (40.8%). SALAD+CM (40.8%), and BoQ (38.2%) show competitive performance, but still remain below method for top-k retrieval metrics. These results demonstrate that our approach maintains robust retrieval under heavy occlusion.
SF-XI--Ocllssion
Method
Desc.dim.
R @
R@5
@ 10
R の 20
GeM
2048
11.8
15.8
17.1
22.4
AnyLoc LoC(VTT-++GeM)
768
6.6
14.5
19.7
26.3
ConvAP
2048
23.7
26.3
28.9
31.6
Mix
4096
30.3
35.5
38,2
44.7
CricaVPR,
4096
40.8
51.3
54.6
59.9
BoQt
512
38.2
50.0
53.3
59.2
SALAD
512+32
31.6
42.1
46.1
51.3
SALAD+CM
512+32
40.8
53.7
58.3
61.3
BigenPlaces
512
36.8
51.8
56.6
59.2
CosPlace
512
32.9
43.4
46.1
48.7
No Classification
512
17.1
25.0
26.3
31.6
MutualVPR (Ours)
512
47.4
65.8
71.1
73.7
Table 2: Comparison on SF-XI--Occlssio, Each query SF-XL--cccusion affected by occlusion, making it suitable for testing robustness under missing visual cues. The upper block lists contrastive learning methods, and the lower block lists classification: based ones. "No Classification" indicates that no mnrr-grrd classification is applied- images within the same grid are treated as belonging to the same class. Best results are shown bold, and second best are underlined.
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 result, the model learns to associate semantically similar scenes across varying view directions, improving label consistency and robustness.
See Appendix B. for a visual example showing a misclassified occluded query being reassigned to the correct class after training.

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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 presented in Appendix B.2
These findings suggest that by iteratively updating class assignments based feature similarity, our approach naturally brings semantically similar scenes scenes-despite despite view differences occlusions- 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-angh cropping.
Tokyo24/7
SF-XI-testvl
Method / Diff.
Backbone
Cropping strategy
R@1
R@5
R@1
R@5
CosPlace
ResNe150
OF
76.5
89.2
64.8
73.1
CosPlace
ResNe150
30"
80.1
90.2
70.1
80.6
CosPlace
ResNe150
0"430*
85.1
92.4
81.1
86.2
Mutual (Ru(O
ResNe150
0"
82.9
90.2
74.5
83.1
Mutual (Ourr)
ResNe150
81.6
91.0
72.4
81.6
MutualVPRR(On (PR(O))s)
ResNe150
0*+30*
85.4
92.5
74.8
82.5
CosPlace
DINOv2
0"
90.2
95.3
76.6
86.3
CosPlace
DINOv2
89.8
95.0
75.9
86.1
CosPlace
DINOv2
0*+30*
91.0
95.8
79.1
86.3
MutualVPR((Ors)
DINOv2
91.1
97.1
77.0
MutualVPR
DINOv2
30"
89.9
96.0
84.4
Outu
DINOv2
0'+30"
92.1
96.5
80.8
86.4
Table 3: Performance Comparison of Ground Truth and Cropping. CosPlace represents method that uses ground-truth and our method represents mutual learning frame. 0" and 30" indicate the starting angles when cropping the panorama.
We conducted experiments using both DINOv2 and ResNet50 as backbones. For each starting angle, cropped the panoramic image into six evenly spaced views; e.g. with starting angles of 0' and 30", this results a total of 12 cropped images. In our method using ResNet50, we clustered all views into six classes (K 6), while for DINOv2, followed the same procedure but set K 3.
While CosPlace sees clear gains from multi-angle cropping on SF-XI--testvl (R@1 up to 81.1%), its performance on Tokyo24/7 (R @ 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 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, larger K results in finer partitioning, increasing the diversity of images within the feature space. In our experiments, we evaluated 1,3, 6, and the corresponding results are shown in Table
Tokyo24/7
SF-XL-testyl
Backhone
R@1
R@5
R@1 1
R@5
ResNe150
68.3
84.8
52.1
62.0
ResNet50
81.0
89.8
73.9
81.4
ResNet50
82.9
90.2
74.5
83.3
DINOv2
80.6
91.4
61.1
71.1
DINOv2
91.1
97.1
77.0
84.6
DINOv2
86.0
94.0
70.9
79.6
Table 4: Different Cluster Numbers. Trained on the original dataset, results with descriptor dimension of 512. K = 1 can be considered as not classifying the dataset, while K = 6 aims to match the number of cluster labels with the ground truth clustering labels.
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 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.
3, outperforming K = by approximately
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 weal-world VPR applications.
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. 1000FA07114022,
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A
Dataset Details
We train several large-scale datasets, including SF-XI for classification-basee methods, GSV- Cities [ for contrastive learning, and evaluate on Pitts30k-test MSLS-val [31], Tokyo 24/7
27
GSV-Cities 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.
summary of above datasets is provided Table
Dataset
Database
Query
SF-XI-ttrain
5.6M
SF-XL-ttest
2M
1000
SF-XL-val
8K
8064
SF-XL-occlusion
2M
76
GSV-Citics-train
560K
Pitts30k-test
10K
6818
MSLS-va val
18.9k
740
Tokyo24/7-test
76K
315
Table Experimental dataset statistics.
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), visualize clustering results before and after training.
Before training
After training
query
21-
query
00-
10
+10
-20
-30
-26
23
20
-30
-28
20
class0
class1
class2
class3
class4
class5
query
Figure 4: The t-SNE visualization of clustering results on SF-XL-Ocelusion before and after training. The query (black dot) is reassigned from class to class 2, correcting its initial misclassifi- cation.
As shown in Figure we select query (black dot) and its neighboring samples from the same geo-grid cell. Initially, the query is assigned class 1, after training, transitions to class These two classes correspond adjacent view directions with overlapping semantic content.
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Query
Database
Before traning:
After traning:
class0
class5
Figure 5: Close-up of the query and its nearest samples. Visual inspection shows class 2 has stronger semantic similarity to the query.
Figure 5 shows visual comparison of samples in class and class 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 occlusion and supervision noise.
This demonstrates that adaptive clustering effectively mitigates the impact of occlusions during training by refining seature-groupinn over time. enables the model to build robust associations between partially occluded and unobstructed views from the same location.
B.2 Feature Distance Analysis Across Classes
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 proxy for selecting image pairs with adjacent labels and minimum mutual distances. As shown in Figure we compare the pairwise feature distances obtained by our method and CosPlace.
Cluster Distance Statistics
Filtered Cluster Distance Statistics
1.2
1.2
2.0
Disance
0.6
0.4
CosPlace
EigenPlaces
Ours
CosPlace
ElgenPlaces
Ours
Figure 6: Visualization results. Feature distance comparisons between adjacent classes, using EigenPlaces as proxy. Our method shows tighter clustering both overall (a) and for similar pairs with d < 0.8 (b), indicating better feature continuity across classes.
21

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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 FigenPlaces, 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- -ineeniinnsi Descriptor
SF SF-XL-Occlusion
SF-XL-testvl
Method
Desc.dim.
R@1
R@5
R@ 10
@ 20
R@1 1
R@5
R R@ @010
R@2 20
BoQ
8192
49.8
65.8
70.3
75.9
82.3
87.9
91.3
92.9
SALAD
8192+256
45.4
62.2
67.1
74.6
82.4
86.6
89.2
90.0
SALAD+CM
8192+256
46.1
57.9
64.5
68.4
80.6
85.3
87.7
89.1
MutualVPR (Ours)
8192
51.4
67.5
72.8
75.8
82.8
88.9
90.8
91.4
Table 6: Comparison on ST-XL-Occlusion and SF-XL-testv1. Comparison with high-dimensional DINOv2-based methods. Our method shows limited improvement when increasing dimensions, but still achieves competitive performance. Best results are shown in bold, and second best are underlined.
Since our method already achieves excellent performance with a relatively low descriptor dimension (512), further conduct fair comparison with SOTA methods under similar dimensional settings, as shown T The original BoQ model has a very high dimensionality of 12288, making infeasible to evaluate 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, the dimension from low (Table I and Table to high, possibly because our dimensionality control is achieved through simple MLP, whereas other methods involve additional internal projection layers.
method shows only moderate improvement when increasing
Nevertheless, our method still delivers outstanding performance, achieving the best results in both R@I and R@5 metrics.
C.2 Fine-tuning Strategies
To assess the effectiveness of our framework in accommodating different fine-tuning strategies, evaluated MulCon the method used in our work for adapting DINOy2 features, against PEFT- based approaches proposed in SelaVPR [21 and EDTformer [16]. Table 7 presents retrieval per- formance across multiple datasets, including Tokyo 24/7, MSLS- Val, Pitts30k, SF-XL-vI, and SF-XL-OOclusion.
Method
Tokyo247
MSLS-val
Pitt30k
SF-XL-v1
SF-XL-Occcusion
SelaVPR
96.1 90.9 96.1
86.4 / 93.6
89.9 /
75.3 84.3
40.5 54.
EDTformer
87.3 93.7
85.5 / 93.8
89.5/95. 95.7
76.4 / 82.7
38.8 / 52.0
MulConV (Ours)
92.1 96.5
89.2 / 95.1
90.9 96.4
80.8 86.4
47.4 65.8
Table 7: Comparison of different fine-tuning strategies within our framework. Metrics are R @
/ 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,
22

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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 MulCon V as the fine-tuning strategy in our framework. These findings highlight both the flexibility and robustness of our approach, showing that 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 feature extractors under consistent training protocol (with K = 3 and no differential cropping). The results are shown in Table g
SF-XL-
Backbone
Params.
Flops.
@@1 1
R@5
VGG16
15.0m
77.5b
61.5
70.8
ResNet50
24.6m
21.3b
73.9
81.4
DINOv2
100.9m
122.8b
77.0
84.6
Table Comparisons of various backbone. Train under different backbones when K=3 on origin dataset without multi-angle cropping. For VGGI6, only the parameters of the last layer are trained. For ResNet50, only the parameters beyond the third layer are trained. For DINOv2, only the adapter module is trained.
Despite having significantly fewer FLOPs than VGG16, ResNer50 achieves much better performance (R@1 of 73.9 vs. 61.5), highlighting the advantage of deeper residual connections and stronger feature representations. DINOv2, vision transformer pretrained with self- self-suuprvised learning, achieves the best retrieval accuracy (R@ I 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 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-testvl
Although our method generally outperforms existing approaches, it shows slight drop on SF-XI.- testvl, where FigenPlaces performs marginally better.
This arises from their methodological difference: of the same focal point from different directions, thus enforcing a strong viewpoint-invarian prior that aligns well with SF-XL's panorama-croppet multi-view structure.
EigenPlaces, being geometry-drivent groups views
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-XI.-testv 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 (09 vs. 180"), as illustrated in Fig.
The average inter-cluster distances were CosPlace: 1. 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-XI-ttstyl. Conversely, CosPlace shows the largest inter-cluster distance and correspond- ingly lower performance, further validating that this metric meaningfully reflects model's sensitivity
23

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0" vs. 180" View Distance Statistics
1.2
Distance
0.6
CosPlace
EigenPlaces
Ours
Figure 7: Comparison of viewpoint invariance. Box plot of inter-cluster distances (09 vs. 180%) for CosPlace, EigenPlaces, and Ours. Smaller values indicate stronger viewpoint invariance.
viewpoint variation. Our method lies between the two, achieving balanced trade-off-less rigid viewpoint invariance but greater robustness to occlusion, scene clutter, and appearance ambiguity.
24


Reviews:
[{"review_id": "HZFGBg1iRb", "reviewer": "Reviewer_uVwD", "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.", "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.\n2. 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.\n3. 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", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. 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. \n2. This method shows good improvement in the VPR task on the testing datasets, especially the one with occlusions. \n3. The paper is well-written and easy to read. \n\nWeakness:\n1. Missing comparison with other DINOv2-based VPR methods, such as AnyLoc and SALAD.\n2. worse than EigenPlaces on SF-XL test v1.\n3. As mentioned in the paper, the number of K is still an open question.", "was_revised": true, "final_justification": "It will be good to include the answers in the revised version, eg, how the NetVLAD is retrained. Most of my concerns are addressed, while limitations remain in the proposed method. Thus, I will keep my score.", "rebuttal": "### Response to Q1: \nWe thank the reviewer for this insightful question, which allows us to clarify an important aspect of our classification-based approach to VPR. We believe there may be a slight misunderstanding regarding the role of the classifier and the use of labels during inference.\n\nAs noted in line 121 of the paper, the classifier is used only during training to guide the network in learning discriminative features. At inference time, we do not use the classifier or any form of  \"test labels\". Instead, retrieval is performed directly using the learned feature descriptors via distance.\n\nRegarding how our labeling strategy impacts recall@k, our dynamic geo-grid labeling ensures that images of the same place, even if captured from diverse and inconsistent viewpoints, may be assigned to different clusters during training. However, since these clusters are geometrically close, the learned descriptors for such images remain nearby in the embedding space. As a result, during retrieval, these images are still within the distance threshold and are correctly recalled.\n\nThis design enhances recall@k by increasing descriptor robustness to viewpoint variation. We have discussed this mechanism in line 203 of the paper and further supported it through visualizations and quantitative results in Appendix B.2.  \n\n### Response to Q2:\nThank you for pointing this out. We clarify that the 131,072-dimensional NetVLAD descriptor reported in Table 1 is correct in our context. Specifically, we re-trained NetVLAD on the GSV-Cities dataset using a ResNet50 backbone (which outputs 2048-dimensional features) and the standard configuration of 64 cluster centers, resulting in a descriptor of size 2048 × 64 = 131,072.\nThe original NetVLAD setup uses a VGG16 backbone with 512-dimensional features and 64 clusters, leading to a descriptor of 512 × 64 = 32,768. This explains the difference in reported dimensions.\n\nRegarding evaluation on SF-XL-testv1, although NetVLAD descriptors can be precomputed offline, nearest-neighbor search requires loading over 2 million database descriptors into memory, which exceeded the hardware limits of our setup and prevented us from running the test.\n\nAs for Pitts250k-test, while we acknowledge its relevance, prior works such as SALAD [1] and CricaVPR [2] often report results on only one of Pitts30k or Pitts250k, given that performance trends are consistent across both. Following this convention, we chose to evaluate on Pitts30k-test. Nevertheless, we appreciate your suggestion and will include Pitts250k-test results in the final version of the paper. Preliminary results for these methods are provided in our response to Reviewer W9pq'Q1.\n\n[1]Lzquierdo S, Civera J. Optimal transport aggregation for visual place recognition[C]/Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 17658-17668.\n[2]Lu F, Lan X, Zhang L, et al. Cricavpr: Cross-image correlation-aware representation learning for visual place recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 16772-16782.\n\n### Response to Q3:\nThank you for this important question regarding our methodology for selecting the number of clusters, K.\nTo clarify, we train our method only on the SF-XL dataset, using a single universal set of hyperparameters (including K, learning rate, etc.) across all experiments. Our ablation study on K (Table 4) deliberately varies both K and the backbone architecture to explore their impact on performance.\n\nInterestingly, we observe that ResNet50-based descriptors tend to produce more clearly defined cluster boundaries, while DINOv2-based features often yield overlapping clusters—in some cases, even classes with different orientation labels appear to fall within a single cluster. This indicates that ResNet50 induces more separable features, whereas DINOv2 tends to pull visually similar images closer in feature space. This observation aligns with Table 4: ResNet50 performs better with K = 6, while DINOv2 performs better with K = 3.\n\nThe optimal value of K is also tied to the intrinsic structure of the training data. For example:\n- SF-XL: This dataset is ideal for our classification task, containing many multi-view images per location. These exhibit viewpoint-induced supervision inconsistencies—precisely the challenge our method is designed to address. For this dataset, K = 6 or 3 is appropriate.\n- GSV-Cities: This dataset lacks the above challenge, as all images from a location share the same orientation. Thus, K = 1 is sufficient and preferred.\n\nIn summary, the optimal choice of K is related to both the characteristics of the dataset and the feature space induced by the backbone. While these trends provide useful guidance, the exact K should still be validated through empirical ablation studies.\n\n### Response to weakness1:\nThank you for the suggestion. We are committed to strengthening our experimental comparisons. We will include comparisons with SALAD and AnyLoc in the final manuscript. Preliminary results for these methods are provided in our response to Reviewer W9pq.\n\n### Response to weakness2:\nEigenPlaces performs better than our method on the SF-XL-testv1 split. As noted in the main text (line 171), we attribute this to overfitting. In the EigenPlaces training pipeline, images are cropped from panoramic views facing the same direction, introducing strong viewpoint diversity. Since SF-XL-testv1 also consists mainly of multi-view test samples, this leads to overfitting.\nHowever, EigenPlaces does not handle occlusion well. When a fixed viewpoint is occluded by obstacles, label inconsistency arises—a core challenge our method is designed to address. This is supported by our 10-point performance gain over EigenPlaces on SF-XL-Occlusion, demonstrating EigenPlaces’ inferior generalization capability under occlusion.\n\n### Response to weakness3:\nRegarding the selection of the number of clusters K, we have provided a detailed discussion in our response to Q3. In future work, we plan to explore different datasets and investigate how to select an optimal or dynamic K, based on dataset characteristics and the structure of the feature space."}, {"review_id": "paOhHFnYmx", "reviewer": "Reviewer_Xqf7", "summary": "The 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", "questions": "- 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?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strenghts\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.", "was_revised": true, "final_justification": "The main concerns I had with the paper were writing clarity (the training set construction was not self-contained, in my opinion), as well as clarity in the adopted baselines for experimentation (backbone of CosPlace, EigenPlaces, which are highly relevant papers), and missing citations.\nThe main strengths of the paper are the proposal of a clustering-based training protocol that foregoes the need for heading labels, and the strong results especially on Night and Occlusion domain.\n\nThe rebuttal saw a thorough discussion, which mainly revolved around (i) providing a better discussion to explain experimental data, especially wrt EigenPlaces which is the most related work, (ii) experimental setup for SALAD, CM, CosPlace baselines and (iii) fine tuning strategy vs adaptation for Dinov2.\nThe initial arguments from the authors raised some concerns, however after discussion the authors provided a clear and thorough analysis of the problem, which solved the main issues for me.\n\nI highly suggest to the authors to incorporate the outcome of the discussion on the above topics in the final version, as well as the missing citations.", "rebuttal": "### Response to Q1:\nThank you for raising this important point.\n\nIn Table 1, we followed the original implementations of CosPlace and EigenPlaces, both of which use ResNet50 as the backbone. To ensure a fair comparison, we additionally performed ablation studies in Table 3, where MutualVPR and CosPlace are compared using both ResNet50 and DINOv2 backbones under consistent data settings.\n\nWe acknowledge that our comparison does not include a version of EigenPlaces with a DINOv2 backbone. While we attempted such experiments, the results were notably suboptimal (e.g., Tokyo247: R@1 = 81.4, R@5 = 90.2; SF-XL-testv1: R@1 = 75.3, R@5 = 83.1). We suspect this may be due to the sampling strategy and fine-tuning procedures employed in EigenPlaces, which may not be compatible with the representational characteristics of DINOv2. Consequently, these results may not reflect the backbone’s true potential. To avoid possible misinterpretation, we opted not to include these results in the main paper. We will clarify this rationale in the final version for transparency.\n\n### Response to Q2:\nThank you for this valuable suggestion.\n\nAs fine-tuning methods are not the core focus of our work, we did not include a detailed comparison in the main paper. However, we had previously conducted experiments comparing MulConV with the PEFT method proposed in [1], and we ultimately chose MulConV due to its superior performance in our setting.\n\nIn response to your comment, we have now conducted additional experiments using the PEFT method from [2].  The following are our experimental results:\n| Method       | Tokyo247     | MSLS-val     | Pitts30k-test | SF-XL-v1     | SF-XL-Occlusion |\n|--------------|--------------|--------------|----------------|--------------|------------------|\n| SelaVPR[1]   | 90.9 / 96.1  | 86.4 / 93.6  | 89.9 / 95.8    | 75.3 / 84.3  | 40.5 / 54.1      |\n| EDTformer[2] | 87.3 / 93.7  | 85.5 / 93.8  | 89.5 / 95.7    | 76.4 / 82.7  | 38.8 / 52.0      |\n| Ours     | 92.1 / 96.5 | 89.2 / 95.1 | 90.9 / 96.4 | 80.8 / 86.4 | 47.4 / 65.8 |\n\nThe results show that MulConV remains better suited to our framework, though other PEFT approaches also perform strongly. This highlights both the effectiveness and flexibility of our proposed framework in accommodating different fine-tuning strategies.\n\nWe will include the results and discussion of all these fine-tuning methods in the final version of the manuscript. Thank you again for your insightful feedback.\n\n[1]Lu F, Zhang L, Lan X, et al. Towards seamless adaptation of pre-trained models for visual place recognition[J]. arXiv preprint arXiv:2402.14505, 2024.\n\n[2]Jin T, Lu F, Hu S, et al. EDTformer: An Efficient Decoder Transformer for Visual Place Recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2025.\n\n### Response to Q3:\nFor Q3, you are correct — our method does involve two forward passes. \nTo clarify, the UTM-based geographic grid is predefined. At the start of each epoch, we sample a subset of UTM grid cells, each of which contains images captured from approximately the same location. For each selected grid cell, we group its images and perform clustering within the group to assign pseudo-labels. We extract features using the current model without gradients (i.e., the feature extractor is frozen during clustering), then assign labels based on clustering results. Once labels are updated, standard supervised training proceeds as usual.\n\nHere is the corresponding pseudocode:\n\n    for epoch in epochs:\n        sampled_grids = sample_grid_cells(dataset) \n        for grid in sampled_grids:\n            images = get_images_from_grid(grid)\n            with torch.no_grad():\n                features = model(images)\n            labels = cluster(features)\n            update_labels(images, labels)\n        for iteration in iterations:\n            train_step()  # standard training with updated labels\n\nThe motivation is that at the early stage, clustering based on initial features may assign visually similar images to different classes or visually dissimilar images to same classes, leading to label inconsistency. By updating the pseudo labels progressively during training, we ensure that the grid-based labels and the learned feature space evolve together, resulting in mutual reinforcement. \n\nWe have conducted supporting experiments to validate our method. Specifically, we selected queries within the same geographic grid and visualized their assigned labels before and after training. Through multiple trials, we observed that some queries were assigned to different classes after training, indicating that label updates did occur. We discussed the benefits of this strategy in Section 4.4, and the corresponding visualizations can be found in Appendix B.1.\n\n### Response to weakness1:\nThank you for highlighting these important references! We will incorporate them into our Related Works section. Specifically, we will add [5] to our comparison results. Regarding [6], while it presents an interesting sample mining strategy, it is tailored for triplet construction in contrastive learning by mining positive samples. This approach is not directly applicable to our classification-based framework, which does not rely on explicit positive/negative sampling.\n\nWe will also include [1–4] in the revised manuscript, including [3] as the citation for the SF-XL Occlusion dataset and [4] for its relevance in parameter-efficient fine-tuning.\n\n### Response to weakness2:\nWe appreciate your constructive feedback on the manuscript’s clarity. We agree that certain parts—particularly the “Problem Analysis” section—can be improved for readability.\n\nSince our clustering visualizations are based on class-level groupings, we believe it is appropriate to retain the term “Class.” However, to reduce confusion, we will clearly define “View X” and “Class Y” prior to our discussion on label inconsistency. Specifically: “View X” refers to the camera viewing direction derived from CosPlace’s heading-based orientation labels (i.e., fixed labels), indicated by Roman numerals. “Class Y” refers to the groups obtained through our clustering process.\n\nWe will revise this explanation in both the main text and Figure 2 to improve clarity for readers less familiar with the task. We also welcome further feedback on how to improve this section.\n\nAdditionally, we will adopt your suggestion to clarify that “manual labeling” refers specifically to using heading-based labels with fixed splits. If you deem it necessary, we are happy to relocate the Related Works section to follow the Introduction for better flow."}, {"review_id": "UhzWpNGhay", "reviewer": "Reviewer_W9pq", "summary": "The 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.", "questions": "Please refer to weaknesses.", "limitations": "Yes, limitations have been included as a part of the paper.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The author's detailed explanation and additional experiment results are appreciated and address most of my concerns. I will maintain my original rating for the manuscript.", "rebuttal": "### Response to weakness1:\nThank you for this suggestion to broaden our experimental comparison. We have conducted additional experiments, and the results for GeM, Conv-AP, SALAD, and AnyLoc are presented below. Following Reviewer uVwD’s recommendation, we also include additional results on the Pitts250k-test dataset. Furthermore, in response to weakness2, we include results for CricaVPR (512-dimensional) under the same training and evaluation settings as our main method.\n\nOur method continues to achieve state-of-the-art performance at an equivalent descriptor dimension and notably demonstrates a substantial advantage on the challenging SF-XL-Occlusion dataset.\n\nFor the baseline setups:\n- GeM: We trained a ResNet50 backbone on the GSV-Cities dataset for 40 epochs.\n- SALAD (512+32): We trained a DINOv2 backbone for 40 epochs on GSV-Cities to ensure consistency with our BoQ (512) training.\n- CricaVPR: We applied PCA to reduce the descriptor dimensionality to 512 for a fair comparison.\n- AnyLoc: The original implementation uses ViT-G/14 with VLAD or GeM. However, ViT-G/14 incurs slow inference on the SF-XL dataset. To maintain practical and comparable settings, we used ViT-B/14 with GeM for evaluation, which provides a balanced trade-off between performance and efficiency.\n\nThe results are as follows:\n\n| Method              | Dim        | Trainset     | Tokyo247      | MSLS-val       | Pitts30k-test  | Pitts250k-test | SF-XL-v1    | SF-XL-Occlusion |\n|---------------------|------------|--------------|----------------|----------------|----------------|----------------|----------------|------------------|\n| NetVLAD             | 131072     | Gsv-cities   | 69.5 / 82.5    | 82.8 / 90.3    | 87.0 / 94.3    | 89.1 / 94.8    | -              | -                |\n| GeM                 | 2048       | Gsv-cities      | 60.3 / 73.7    | 72.5 / 82.7    | 84.5 / 92.8    | 85.1 / 93.4    | 25.6 / 35.5    | 11.8 / 15.8      |\n| AnyLoc   | 768      | -            | 71.7 / 87.6    | 32.6 / 41.6    | 77.7 / 88.9    | 79.3 / 89.5    | 33.3 / 45.2    | 6.6 / 14.5       |\n| ConvAP              | 2048       | Gsv-cities   | 74.6 / 83.2    | 81.5 / 87.5    | 89.7 / 95.2    | 91.2 / 96.4    | 41.1 / 53.0    | 23.7 / 26.3      |\n| MixVPR              | 4096       | Gsv-cities   | 87.0 / 93.3    | 87.1 / 91.4    | 91.6 / 95.5    | 94.3 / 98.1    | 69.2 / 77.4    | 30.3 / 35.5      |\n| CricaVPR*           | 4096       | Gsv-cities   | 93.0 / 97.5    | 90.0 / 95.4    | 94.9 / 97.3    | -              | -              | -                |\n| CricaVPR1           | 4096       | Gsv-cities   | 89.5 / 94.6    | 88.5 / 95.1    | 91.6 / 95.7    | 94.3 / 98.6    | 72.8 / 80.1    | 40.8 / 51.3      |\n| CricaVPR1           | 512(PCA)        | Gsv-cities   | 87.4 / 92.9    | 87.1 / 92.6    | 90.4 / 94.9    | 92.5 / 97.1    | 68.4 / 77.1    | 36.9 / 48.1      |\n| BoQ†                | 512        | Gsv-cities   | 91.9 / 95.5    | 88.4 / 93.9    | 93.1 / 96.1    | 93.8 / 97.5    | 79.6 / 85.9    | 38.2 / 50.0      |\n| SALAD               | 512 + 32   | Gsv-cities   | 92.3 / 95.1    | 88.5 / 94.2    | 90.6 / 95.1    | 92.1 / 97.0    | 70.2 / 77.7    | 31.6 / 42.1      |\n| EigenPlaces         | 512        | SF-XL        | 84.8 / 94.0    | 88.1 / 92.9    | 92.3 / 96.1    | 93.5 / 97.5    | 83.8 / 89.6    | 36.8 / 51.8      |\n| CosPlace            | 512        | SF-XL        | 76.5 / 89.2    | 84.4 / 90.2    | 89.6 / 94.9    | 90.4 / 96.6    | 64.8 / 73.1    | 32.9 / 43.4      |\n| MutualVPR (Ours)    | 512        | SF-XL        | 92.1 / 96.5    | 89.2 / 95.1    | 90.9 / 96.4    | 92.6 / 97.9    | 80.8 / 86.4    | 47.4 / 65.8      |\n\nAll methods will be included in the final manuscript, along with a detailed description of the experimental setup.\n\nSince GCL and SFRS are difficult to scale for training on the GSV-Cities dataset, we do not include them in the main experiments to ensure fairness. However, in response to the reviewer’s suggestion, we present their testing results and experimental settings here for reference.\n\nFor GCL(ResNet50+GeM), we directly used the official pretrained weights and evaluated under our testing protocol.\nSFRS has a high feature dimensionality (32768), which makes it difficult to test on the SF-XL dataset. Therefore, we used the official pretrained weights and applied PCA to reduce the dimension to 4096. However, this dimensionality reduction led to a significant performance gap compared to the results reported in the original paper.\n\nThe results are as follows:\n\n| Method | Train-set | Dim       | Tokyo247   | MSLS-val   | Pitts30k-test | Pitts250k-test | SF-XL-v1   | SF-XL-Occlusion |\n|--------|-----------|-----------|------------|------------|---------------|----------------|------------|-----------------|\n| SFRS   | MSLS      | 4096 (PCA)| 36.2 / 50.8| 58.2 / 66.6| 76.0 / 86.8   | 78.2 / 88.2    | 14.0 / 21.9| 5.3 / 11.8      |\n| GCL    | Pitts250K | 2048      | 42.3 / 60.8| 64.0 / 76.6| 71.3 / 86.7   | 67.4 / 83.7    | 4.1 / 8.3  | 2.6 / 5.3       |\n\n### Response to weakness2:\nThank you for highlighting this important point.\n\nOur primary baselines are CosPlace and EigenPlaces, both of which typically use 512-dimensional descriptors. To ensure consistency and fairness, we adopted the same dimensionality across our main comparisons.\n\nFor BoQ, which also uses DINOv2 as the backbone, we chose 512 dimensions to allow for a fair and consistent comparison under the same training and evaluation settings. Although CricaVPR also uses DINOv2, its performance is known to be sensitive to batch size. Since our evaluation uses a batch size of 1, we followed the official CricaVPR setup and retained the 4096-dimensional descriptors to match their published settings.\n\nIn response to your suggestion, we have now included CricaVPR (512) in our evaluation, and the results are provided in our response to weakness1.\n\nFor methods such as MixVPR, which use ResNet50, we retained their original feature dimensionality to preserve the integrity of their published configurations.\n\nAdditionally, we are currently training a version of our method using 4096-dimensional descriptors to explore performance at higher dimensions. However, due to time constraints during the rebuttal period, training is still in progress and results are not yet available. We will include these results and discussions in the final version of the manuscript.\n\n### Response to weakness3:\nThe omission of the GeM reference was our mistake. We have added GeM as a baseline and will include the proper citation.\n\n### Response to weakness4:\nThank you for this suggestion. Our use of LMCL classifier follows the precedent set by CosPlace, as we mentioned in the final paragraph of Section 3.2.2. Similarly, we employed GeM pooling as it is a standard and widely-adopted aggregation method in the VPR field.\n\nWe intentionally kept the descriptions of these modules concise, as our primary focus is on our mutual learning framework rather than  established components. However, we agree with your that providing more detail would improve the paper's accessibility for readers who may not be deeply familiar with this specific domain.\n\nTherefore, we will accept your valuable suggestion and add a more detailed description of both the LMCL classifier and GeM pooling to the appendix in the final manuscript.\n\n### Response to weakness5:\nThank you for catching the color inconsistency in Figure 2; we will correct it in the revision.\n\n### Response to weakness6:\nWe will add all relevant training parameters to the \"Implementation Details\". Thank you for your suggestion."}, {"review_id": "C7ibuKeGak", "reviewer": "Reviewer_6zzo", "summary": "The 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.", "questions": "*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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "*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)", "was_revised": true, "final_justification": "I have read the authors response as well as the comments from the other reviewers. The authors have addressed my main questions and overall reasons to accept the paper overweigh reasons to reject it - interesting problem, relatively novel approach and superior performance. I thus keep my original rating.", "rebuttal": "### Response to Q1:\nYes. CosPlace is our most direct baseline, and our work can be seen as replacing its fixed, orientation-based labeling with our adaptive clustering method. The results in Table 1 and Table 2 demonstrate the overall effectiveness of our approach. For the most direct comparison, Table 3 explicitly ablates the performance of our method against CosPlace on the same backbones (DINOv2 and ResNet50), clearly isolating and validating the significant contribution of our adaptive clustering.\n\n### Response to Q2:\nThis is a very interesting and insightful suggestion. We have not yet explored the application of our method in a continual learning setting, but we agree it is a promising direction. Using our adaptive clustering to refine labels over time as a system encounters new data could be a way to handle long-term appearance changes. We thank you for this idea and welcome further discussion on its potential.\n\n### Response to weakness1:\nWe thank you for highlighting this ambiguity. We would like to clarify: We do not propose a new architecture from scratch. Our contribution is a training framework, MutualVPR, which integrates adaptive clustering with a feature extractor in a mutual learning loop. As detailed in Section 3.1, this framework is a training methodology applied to classification-based architecture. Our novelty lies in the co-evolution of clustering and feature learning to resolve supervision inconsistency, not in the base architecture itself.\nWe first demonstrate the effectiveness of our method by comparing it with current state-of-the-art approaches in Table 1, where our method consistently outperforms others across benchmarks.\n\nTo isolate the contribution of our adaptive clustering, we compare with CosPlace in Table 3, which uses a fixed labeling strategy. This serves as an ablation, showing the benefit of our dynamic labeling. Additionally, the case of K=1 in Table 4, equivalent to disabling our clustering, leads to clearly worse performance, further confirming the value of our method.\n\nWe acknowledge the importance of evaluating a broader set of baselines. In response, we have already included additional comparisons with both classification-based and contrastive learning methods. As reflected in Reviewer W9pq’s comments, our method still achieves state-of-the-art performance under the same feature dimensionality, demonstrating its robustness and generality. We will continue to incorporate more baseline comparisons in future revisions to further validate the effectiveness of our approach.\n\n### Response to weakness2:\nWe thank you for this point and agree that the fixed nature of K is a limitation, which we have acknowledged in the paper. We conducted an ablation study in Section 4.5.2 to analyze its impact.\n\nGenerally estimate the optimal K is currently infeasible and has to be decided by ablation. However, we found that the choice of an optimal K is related to the dataset's characteristics and the backbone.\n\nOn the SF-XL dataset, as regarding to the backbone, interestingly, the descriptors from ResNet50 exhibited more clearly defined cluster boundaries, while those from DINOv2 often showed overlapping clusters — in some cases, even two classes with different orientation labels appeared to belong to a single cluster. This suggests that ResNet50 tends to produce more distinct separations, whereas DINOv2 brings similar-looking images closer in the feature space.This observation corresponds well with our results in Table 4, where ResNet50 performs better with K=6, and DINOv2 performs better with K=3. \n\nWhile these findings provide some insights, we do not yet have a definitive criterion for selecting the optimal K. Therefore, we still regard the choice of K as a limitation of our approach.\n\n### Response to weakness3:\nWe appreciate your comments and would like to clarify that on the SF-XL-Occlusion dataset, our method achieves the best performance, which strongly supports our core motivation of preserving label consistency. Detailed discussions can be found in Section 4.4 and Appendix B.1–B.2.\n\nIn contrast, EigenPlaces performs better than ours on the SF-XL-testv1 split. As noted in the main text (line 171 in the paper), we attribute this to overfitting. Upon further analysis, we find that SF-XL contains multi-view training samples. Following the EigenPlaces pipeline, images are cropped from panoramic views facing the same direction, which introduces strong viewpoint sensitivity. Since SF-XL-testv1 also consists primarily of multi-view test samples, this leads to overfitting.\n\nHowever, EigenPlaces’ sampling strategy does not handle occlusion well. When the viewpoint is fixed and occluded by obstacles, label inconsistency arises — a core challenge our method addresses. This is evidenced by our 10-point performance gain over EigenPlaces on SF-XL-Occlusion, highlighting its inferior generalization ability.\n\nAnyway, we agree that this reasoning should be made explicit, and we will include this analysis in the appendix for clarity.\n\n### Response to weakness4:\nThis is an insightful question. Taking contrastive learning — another representative paradigm in VPR — as an example, we can better understand the distinction between learning paradigms. In contrastive learning, samples are divided into positive and negative pairs, and the goal is to increase the distance between them. However, distances among positive samples are typically not explicitly constrained, which gives rise to a variety of hard sample mining strategies.\n\nIn contrast, classification-based methods enforce clear class boundaries, naturally encouraging samples within the same class to cluster more tightly, though the separation between different classes may be less structured. As demonstrated in Section 4.4 and Appendix B.1, our method leverages this structure to pull semantically similar samples (e.g., different views of the same place) closer in the feature space, thereby improving retrieval performance.\n\nWhile our work is built upon the classification paradigm, we believe the core idea — dynamically aligning semantically similar instances — could be extended to enhance contrastive learning as well, for instance by informing hard negative mining with our learned clusters. \n\nThis is beyond the current scope of our study, but we consider it a promising direction for future research.\n\n### Response to sugesstions:\nThank you for your helpful suggestions.\nFor suggestion1, we have addressed it in our response to Weakness 1, and we have added new baselines. The corresponding results are included in our response to Reviewer W9pq.\n\nFor suggestion2, we have responded under Weakness 3, and we will include the related discussion in the appendix."}]

Author rebuttal:

Meta-review: The paper introduces a novel mutual learning framework for Visual Place Recognition (VPR) that eliminates the need for manual heading labels by integrating unsupervised K-means clustering with descriptor learning. Leveraging a frozen DINOv2 backbone and lightweight adapters, the method iteratively refines clustering and representation learning to overcome label inconsistency caused by viewpoint variations and occlusions. MutualVPR achieves state-of-the-art results, particularly on occlusion-heavy datasets, without relying on handcrafted rules or explicit supervision.

Reviewers praised the method’s novel unsupervised labeling strategy that aligns supervision with geographic locations and co-learns clustering and visual representations. The approach removes the need for heading labels, which are often difficult to obtain, and still shows strong improvements in VPR performance, especially on datasets with occlusions. The method maintains compact descriptors and trains only lightweight components on top of a frozen DINOv2 backbone, offering a parameter-efficient solution. The paper is described as well-written and easy to read, with clear motivation and convincing qualitative and quantitative results that demonstrate the value of the proposed framework.

Several reviewers noted missing comparisons to key recent works, including other DINOv2-based and high-performing VPR methods, which weakens the empirical positioning. The clustering process relies on a static K hyperparameter without principled selection or sensitivity analysis, and the paper lacks ablations that isolate the contributions of clustering versus the feature extractor. Some baselines were compared using lower-dimensional descriptors, which may bias the results, and certain architectural components (e.g., GeM, LMCL) were underexplained. Additional clarity in notation, more formal problem setup, and better placement of related work were also suggested to improve accessibility and rigor.

The authors provided a detailed and constructive rebuttal that addressed all major points.  They clarified design choices (e.g., descriptor dimensionality, clustering strategy), added experiments on additional datasets and methods (e.g., SALAD, AnyLoc, Pitts250k-test), and explained performance trade-offs with respect to methods like EigenPlaces. The mutual learning framework was recognized as novel and effective for handling supervision inconsistency in classification-based VPR, particularly under occlusions and viewpoint variation. With strengthened empirical results and clear revisions promised, this paper received three borderline accepts and one accept -- an acceptance consensus.
Decision: Accept (poster)