SAGE: Spatial-visual Adaptive Graph Exploration for Efficient Visual Place Recognition
Authors: Shunpeng Chen
Changwei Wang
Rongtao Xu
Peixingtian
yukun Song
Jinzhou Lin
Wenhao Xu
jingyizhang
Li Guo
Shibiao Xu
Venue: ICLR 2026 Poster (2026)
Keywords: Visual Place Recognition
Foundation Model
Parameter-Efficient Fine-Tuning
Autonomous Driving
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel and efficient visual place recognition approach that incorporates adaptive graph mining, achieving SOTA performance with Parameter-Efficient Fine-Tuning (PEFT) and compact descriptors.

Abstract: Visual Place Recognition (VPR) requires robust retrieval of geotagged images despite large appearance, viewpoint, and environmental variation. 
Prior methods focus on descriptor fine-tuning or fixed sampling strategies yet neglect the dynamic interplay between spatial context and visual similarity during training.
We present SAGE ($\underline{S}$patial-visual $\underline{A}$daptive $\underline{G}$raph $\underline{E}$xploration), a unified training pipeline that enhances granular spatial-visual discrimination by jointly improving local feature aggregation, organize samples during training, and hard sample mining. 
We introduce a lightweight Soft Probing module that learns residual weights from training data for patch descriptors before bilinear aggregation, boosting distinctive local cues.
During training we reconstruct an online geo-visual graph that fuses geographic proximity and current visual similarity so that candidate neighborhoods reflect the evolving embedding landscape. 
To concentrate learning on the most informative place neighborhoods, we seed clusters from high-affinity anchors and iteratively expand them with a greedy weighted clique expansion sampler.
Implemented with a frozen DINOv2 backbone and parameter-efficient fine-tuning, SAGE achieves SOTA across eight benchmarks. Notably, our method obtains 100% Recall@10 on SPED only using 4096D global descriptors.
The code and model are available at https://github.com/chenshunpeng/SAGE.

Published as a conference paper at ICLR 2026
SAGE: SPATIAL-VISUAL ADAPTIVE GRAPH EXPLO- RATION FOR EFFICIENT VISUAL PLACE RECOGNITION
Shunpeng Chen', Changwei Wang², Rongtao Xu², Xingtian Pei', Yukun Song'
Jinzhou Lin', Wenhao Jingyi Zhang', Li Guo', Shibiao Xu"
1 School of Artificial Intelligence. Beijing University of Posts and Telecommunications
2 Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology "Spatialtepprral AI shunpengchen@bupt.ccm edu. cn, shibiaoxu@bupt.. eduttedddcc.c
ABSTRACT
Visual Place Recognition (VPR) requires robust retrieval of geotagged images despite large appearance, viewpoint, and environmental variation. Prior meth- ods focus on descriptor fine-tuning or fixed sampling strategies yet neglect the dynamic interplay between spatial context and visual similarity during training. We present SAGE (Spatial-visual Adaptive Graph Exploration), unified training pipeline that enhances granular spatial-visual discrimination by jointly improving local feature aggregation, organize samples during training, and hard sample min- ing. We introduce lightweight Soft Probing module that learns residual weights from training data for patch descriptors before bilinear aggregation, boosting dis- tinctive local cues. During training reconstruct online geo-visual graph that fuses geographic proximity and current visual similarity so that candidate neigh- borhoods reflect the evolving embedding landscape. To concentrate learning on the most informative place neighborhoods, we seed clusters from high-affinin an- chors and iteratively expand them with a greedy weighted clique expansion sam- pler. Implemented with a frozen DINOv2 backbone and parameter-efficient fine- muning, SAGE achieves SOTA across eight benchmarks. Notably, our method ob- tains 100% Recall 10 on SPED only using 4096D global descriptors. The code and model are available at ht https: / //gggtthuu/ com/ chenshunpeng/ SAGE.
INTRODUCTION
Visual Place Recognition (VPR) matches query image to its corresponding location within large- scale geotagged database, serving as a fundamental capability for critical applications such as au- tonomous robot navigation (Han et al., 2025), loop closure detection for autonomous driving (Teng et al., 2026), and large-scale map construction (Zhu et al., 2024). The main challenge of VPR is maintaining robust retrieval performance under severe and unconstrained environmental changes, including extreme viewpoint shifts, illumination variations, adverse weather, long-term temporal drift, and frequent dynamic occluders, among others (Liu et al., 2024; Zhu et al., 2025).
Early VPR methods relied on hand-crafted local deseriptors (Lowe, 2004; Bay et al., 2008) and ag- gregated them into global encodings via pooling schemes such as Bag of Words VLAD (Angeli et al., 2008; Jégou et al., 2010). However, these methods lack adaptability and perform poorly under large-scale uppearance changes. With the advent of deep learning, learnable aggregation modules (Arandjelovic et al., 2016; Radenović et al., 2018) were introduced, which enhanced descriptor com- pactness and robustness by learning task-specific pooling strategies. Subsequent research has mainly focused on simplifying, regularizing, or refining aggregation mechanisms to improve generalization and computational efficiency (Jin et al., 2025b), such as reducing reliance on explicit cluster centers or alleviating the "burstiness" problem of local features (Lu et al., 2024d; Khaliq et al., 2024).
Recently, the advent of Visual Foundation Models (VFMs) (Dosovitskiy et al., 2020; Oquab et al., 2023) has advanced VPR by enabling the capture of long-range semantic dependencies and richer
Corresponding author.

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Figure/ 1: Performance and parameter efficiency of SAGE. (a-d) Recall across four datasets at different global descriptor dimensions; SAGE achieves the best performance regardless of backbone and descriptor size. (e) Parameter comparison. By freezing DINOv2, SAGE substantially reduces trainable parameters compared to methods employing adapters or partial encoder tuning methods, demonstrating high efficiency. (f) Recall @ 1 performance compared with EMVP across the datasets.
interactions between image patches (Ali-bey et al., 2024; Lu et al., 2025). These strategies reduce sensitivity to occlusion and background clutter and improve robustness with controlled parame- ter overhead. While recent adaptation methods for VFMs are notably efficient, different strate- gies within the broader VFMs ecosystem vary in resource consumption (Jin et al., 2025a). For in- stance, fine-tuning backbone's encoder layers typically requires more computational resources than Crrameter-Efficien
Tuning (PEFT) approaches (Jia et al., 2022; Qiu et al., 2024). Furthermore, while the model's understanding is dynamic, a pre-defined static sampling policy (lzquierdo & Civera, 2025) may fail to consistently present the most informative examples as learning progresses.
Recent research has explored constructing training batches that reflect real-world difficulty. How- ever, a common strategy operates on a static "think-once, act-always" principle, relying on offline computations like pre-defined clustering based on initial features (Liao Shao, 2022; Leyva- Vallina et al., 2023; Tzachor et al., 2025; Wang et al., 2025; Izquierdo & Civera, 2025). Such approaches often treat geographic priors and visual similarity independently, overlooking crucial fact: what constitutes a real-world hard sample is governed by the intricate, dynamic interplay between spatial proximity and visual ambiguity. This hardness is not fixed property, but state that evolves with the model's 's embedding geometry during training. Effective mining thus requires architectural "slow thinking" involving an iterative reassessment of difficulty. Without this, static methods gradually be- come obsolete. They continue feeding the model stale examples, wasting computational resources and limiting learning efficiency, as old challenges turn trivial and new ones emerge at the decision boundary. Ultimately, the inability to concurrently enhance granular local expressiveness and dy- namically align the sampling process with the evolving geo-visual landscape leaves a significant gap in current training paradigms, potentially limiting their adaptability in real-world scenarios.
To address these interconnected limitations, we propose SAGE (Spatial- Visual Adaptive Graph Ex- ploration), a unified VPR training framework that embraces a "slow thinking" paradigm for hard sample mining. Rather than depending on a one-time, fixed policy that labels samples as hard for the entire training run, SAGE continuously revisits and updates the hardness labels in response to changes in the model's 's representation. This philosophy is realized through fundamentally dynamic architecture. At its core, an online process reconstructs a geo-visual affinity graph each epoch, en- suring the sampling strategy stays synchronized with the model's 's evolving embedding space. To maximize the impact of this intelligent sampling, SAGE also incorporates lightweight modules, in- cluding Soft Probing (SoftP) and an InteractHead, which enhance descriptor quality by amplifying discriminative local patches and modeling cross tross-image associations. This synergy between dynamic mining and enhanced feature representation allows SAGE to focus learning on the most informa-
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tive spatial-visual neighborhoods, leading to sfate-of-the-art accuracy with remarkable parameter efficiency, as summarized in Fig. 1. In summary, the main contributions of this paper are:
SoftP Feature Interaction. We propose SoftP, a lightweight module that uses data-driven residual weighting to enhance discriminative local patches, and an InteractHead that models associations between fragments across images, thereby improving descriptor coherence across views.
Dynamic Geo- Visual Graph Mining. Our online strategy dynamically rebuilds the geo-visual affinity graph each epoch, keeping the mining process aligned with the model's evolving embed- ding space while prioritizing the most informative samples for faster convergence.
Weighted Greedy Clique Expansion. Our weight-guided algorithm initiates sampling from an- chors with high affinity and iteratively expands the most challenging neighborhoods, thereby gen- erating balanced batches of utility that focus learning on detailed spatial and visual distinctions.
Efficient SOTA Accuracy. Implemented with frozen DINOv2 backbonc and parameter-efficien fine-tuning, SAGE sets a new SOTA across eight challenging VPR benchmarks (Fig. 1), retaining robust retrieval competitiveness even with highly compact descriptors for large-scale deployment.
2 RELATED WORK
Visual Place Recognition (VPR) requires global descriptors to remain compact and robust under substantial variations in viewpoint, illumination, and scene structure. Early methods for generating global descriptors, such as NetVLAD (Arandjelovic et al., 2016), utilized a vast set of cluster cen- ters, which rendered them vulnerable to domain shifts. Although supe-supe hard sample min- ing (Ge et al., 2020) has been introduced to mitigate this issue, such methods remain constrained by the representational capacity of CNN backbones, yielding suboptimal performance. With the incorporation of spatial attention (Noh et al., 2017) and feature reweighting (Ng et al., 2020), de- scriptor robustness has been yet improved. Other works focus on optimizing feature aggregation (Ali-Bey et al., 2023b; Izquierdo & Civera, 2024; Ali-bey et al., 2024), which improves perfor- mance but relies on fixed aggregation strategies and thus lacks adaptability to dynamically evolving embeddings. Recent studies have highlighted the effectiveness of modulating feature magnitudes in manner prior to their aggregation. Such approaches include non-local
a lightweight, data-dependen attention for adaptive spatial weighting (Chen et al., 2023), efficient context encoding (Huang al., 2022), parameter-efficient tuning with second-order moments (Gao et al., 2023), and the exponen- tially weighted fusion of pooling kernels (Stergiou & Poppe, 2022). Other methods reduce overhead by lowering the number of clusters or entirely eliminating clustering through centroid-fre probes (CFP) (Lu et al., 2024d; Qiu al., 2024), yielding compact descriptors by utilizing second-order feature statistics. Two-stage approaches (Wang et al., 2025; Lu et al., 2024a) improve retrieval ac- curacy through local feature re-ranking but introduce additional computational overhead. Recent single-stage methods can achieve comparable or even superior performance using only global fea- tures (Berton & Masone, 2025; Liu et al., 2025b). This pursuit of efficiency is also reflected in adapting powerful Visual Foundation Models (VFMs) for downstream tasks (Zhang et al., 2025; Liu et al., 2025a). With the proliferation of VFMs, Parameter- Effficiint Fine-Tuming (PEFT) has emerged as crucial paradigm. Instead of fine-tuning the entire backhone, these methods update only a small subset of parameters, such as lightweight adapters or normalization layers, thereby significantly en- hancing training efficiency (Jia al., 2022; Qiu et al., 2024). Our method follows this paradigm by freezing the backbone while introducing lightweight modules to enhance feature discriminability. Cross-image correlation methods (Lu al., 2024b; Qiu al., 2024) further enhance matching by capturing and modeling inter- image dependencies. In line with this paradigm, our approach strate- gically enhances feature discriminability. We introduce Soft Probe, a lightweight residual module that adaptively amplifies salient local regions, and InteractHead, which models cross-image depen- dencies. Together, they significantly boost descriptor quality with minimal parameter overhead.
In deep metric learning, dynamic sampling strategies (Liang et al., 2021) adjust the importance of training pairs with epoch-dependent weighting terms, organizing them in an "easy-to-hard" order, which enables the network to first learn general category boundaries from easy samples and then focus on hard samples in later stages. More recently, a graph-based sampling method (Liao & Shao, 2022) has been proposed, which constructs nearest neighbor graph from class embeddings at the beginning of each epoch. By selecting an anchor class and its neighboring classes to form train- ing batches, this approach improves the discrininative power of learned embeddings and enhances
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Figure 2: SAGE overview. (a) Pipeline: a frozen DINOV2 with PEFT outputs tokens; SoftP am- plifies informative patches, and InteractHead applies cross-image attention form a robust global descriptor. (b) Online Graph Creation: each epoch builds geo-visual affinity graph, keeping top- k neighbors and updating edges as embeddings evolve. (c) Greedy Weighted Sampling: seed by average affinity and expand cliques by adding the most connected nodes. (d) SoftP: A lightweight module that uses residual weighting to emphasize discriminative features prior to aggregation.
training efficiency. In VPR, spatial graphs have been used to encode geographic relationships. For example, the MMS-VPR benchmark (Ou et al., 2025) represents street intersections and road seg- ments as nodes and edges, leveraging topological context to improve retrieval performance. Such approaches often rely on mining discriminative regions or focusing on hard positive samples (Lu et al., 2023; Seidenschwarz et al., 2021; Fang et al., 2022; Wang et al., 2025) to enhance accuracy and robustness, but ignore geo-information. Other works reformulate VPR as classification task (Berton et al., 2022a; 2023) to avoid explicit mining, but these remain limited by static sample se- lection and the neglect of geographic information. Moreover, sparse datasets such GSV-Cition (Ali-bey et al., 2022), these limitations restrict generalization capability. On dense datasets such as MSLS (Warburg et al., 2020), offline clustering methods (Izquierdo & Civera, 2025) partition visu- ally similar and geographically neighboring images into fixed clusters for training. Another category of approachees leverages hybrid strategies (Kalantidis et al., 2020), hard negative mining (Garg al., 2022; Deuser et al., 2023; Ali-Bey et al., 2023a) or generation (Peng et al., 2024) to enhance retrieval performance. However, most of these approaches struggle to generalize effectively. In contrast to such static or scheduled strategies, SAGE reconstructs geo- vioovisual graph at every training epoch and employs greedy sampling to focus on the most densely populated and challenging clusters in the evolving embedding spaces, achieving superior performance across datasets.
3 METHOD
Fig. 2 illustrates the proposed SAGE framework. First, the frozen DINOv2 feature extraction back- bone processes input images (S)..3.1). Next, the Soft Probing (SoftP) module aggregates these features into robust global descriptors (Sec.3.2). Then, Online Graph Creation employs Interact- Head to refine descriptors and integrates geographic and visual distances to form dynamic graphs (Sec.3.3). Finally, we adopt greedy weighted sampling to focus training on hard examples (Sec.3.4).
3.1 FEATURE EXTRACTION
DINOv2 provides strong visual representations from large-scale relf-supervised pretraining. We use pretrained DINOv2 as a frozen backbone and achieve parameter-efffiient fine-tuning by inserting learnalle Dynamic Power Normalization (DPN) layers into the last N encoder blocks (Oquab et al., 2023; Qiu et al., 2024). As shown in Fig. 2(a), an input image E Hx x3 passes through two stages. Uncalibrated Blocks extract base features, and Recalibrated Blocks integrate DPN to recalibrate features and produce task-specific representations. The backbone outputs one learnable class token and L patch tokens, forming token matrix f (I++1)x where M is the embedding dimension. SoftP and InteractHead then aggregate and refine these tokens to strengthen gross-image correspondence and yield discriminative global descriptor F TR DxDx for place recognition.
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3.2 SOFT PROBING
Centroid-Free Probing (CFP) crafts a global image descriptor f by aggregating the L local spatial descriptors {X+ into second-order Gram-like covariance matrix, which eliminates the need for offline cluster centers (Qiu et al., 2024; Lu et al., 2024d). Although related methods like Moment Probing also leverage second-order statistics (Wang al., 2022: Gao et al., 2023), a key limitation of CFP is its uniform treatment of all descriptors. This equal weighting can underemphasize subtle yet discriminative local cues. To address this shortcoming, we introduce Soft Probing (SoftP), lightweight module shown in Fig. 2(d), which adaptively emphasizes informative spatial locations while preserving the underlying feature geometry before the final aggregation step.
SoftP first computes a scalar response for each descriptor and converts it into bounded residual coefficient; this coefficient is broadcast across channels and applied in residual form to obtain the modulated descriptors. Concretely, for each descriptor X; we compute an lg response s, ||X2 + e (with æ > 0 for numerical stability), and feed s; into a compact predictor e (a two-layer MLP) that outputs scalar which is squashed by sigmoid and scaled by hyperparameter a:
3. = a- d((s;)))
S 3 S a.
(1)
The modulated descriptor formed residually as:
X = X; + +3 = (1 + 3,)
(2)
This residual reweighting behaves like a soft, data-driven attention mechanism: amplifies salient responses while avoiding destructive rescaling of channel structure (Ng et al., 2020). Under mild assumptions (or to first order when 3; is small and the mean shift is negligible) (Gao et al., 2023), the variance of each dimension of the modulated descriptors increases approximately as:
Var((X(.))
1 E (1 + 28.) || - XI| N
Σ X. N
1
X||
2
8
X
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Var((X.))
where x E, Xr This relation highlights that SoftP selectively enlarges the variance contribu- tion of high-response locations, thereby enhancing the sensitivity of subsequent aggregation stages to discriminative local structures. Finally, the set of modulated descriptors {X₂} is passed to the aggregation stage to produce the final global descriptor. SoftP adds only a negligible number of parameters, preserves the semantic geometry of the original descriptors, and consistently improves the robustness of the resulting global descriptor under significant appearance changes.
3.3 ONLINE GRAPH CREATION
To prepare for the graph creation, the image descriptors f; E R produced by SoftP are first processed by the InteractHead. Departing from prior method that partition features via learned cluster assignments(Lu et al., 2024b;d), InteractHead deterministically splits each descriptor into S fixed length segments, {r("))__ j==1- To enable cross-image attention, these segments are rearranged such that for each index s, the segments from all B images form a sequence. These sequences are then processed by a two-layer Transformer encoder (E) with GELU activation. This encoder structure applies attention across the batch for each segment type, capturing consistent correlations across views and improving descriptor robustness. The enhanced descriptors F are obtained by:
F reshape (((EE)) ......
((5)
Our approach continuously aligns candidate graphs to the model's current embedding space by constructing the graph at each training epoch. First, for each city we group images by their unique cluster labels and randomly sample one image from each cluster. The sampled images are passed through our model to obtain descriptors that serve as representative features for the clusters, produc- ing set of cluster-level features for every city.
The process begins by sampling cities with probability proportional to their cluster count. From chosen city, randomly select a single cluster, termed "place". We then identify P similar
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places by computing cosine distances between the descriptor of selected place and all other cluster descriptors, and sampling probabilistically such that smaller distances yield a higher selection probability. The images from these P + total places become the unordered nodes of graph, for which we compute all pairwise Fuclidean geographic distances (1.o) Subsequently, we construct adjacency graph by connecting nodes whose geographic distance below threshold From this graph, we extract several cliques (i.e., complete subgraphs), denoted as G = (V, E). Finally, within each clique, we calculate the pairwise visual descriptor distances as dvia(i, [|F; FFll2- 212.. To combine these two distance types and do so multiplicatively, we define:
Wi - (dgoo (8, dhvis (1, Wi: 0.
As illustrated in Fig. 2(b), we construct a sparse affinity graph g = (V, E"), where an edge (i. exists if its affinity score Way exceeds predefined threshold 72. This graph is dynamic, as it is rebuilt each epoch to reflect the continuous evolution of the model's 's embeddings. From this graph, clique, for the sampling process. The search concludes upon finding the first clique C' that meets a minimum size requirement of |Vcl 2 N = 10. This clique is then used guide the subsequent sampling stage.
seek complete subgraph, known
3.4 GREEDY WEIGHTED SAMPLING
We propose greedy, weight-driven selection process that adaptively focuses training on the most informative neighborhoods while fully leveraging the reconstructed geo-visual graph. We first iden- tify the most central node in the graph to serve as cluster anchor. This is achieved by computing a seed score S(i) for each node, which represents its total affinity all other nodes:
5(1)
1 N
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Fig. 2(c) shows the node with the highest score is chosen as the initial member of our training clique, C' = (25 }. where vn arg max; S(i). Subsequently, we iteratively expand the clique C' by adding the node v² that exhibits the highest average affinity the current members of C::
arg max eCC
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This procedure repeated until the clique reaches the desired size |C|= k; where k = 4. By seeding from central anchor and greedily expanding towards the closest nodes, our method effectively drills down into the densest subgraphs of the geo-visual landscape. These dense regions represent clusters of mutually confusing samples the most difficult scenarios where the model struggles to make fine- gr-igniiiddd distinctions. Our approach is inherently adaptive to the model's 's learning progress. It dynamically responds to the evolving weight distribution each epoch, concentrating training effort on the most pertinent hard positive and negative examples. This adaptive focus not only accelerates convergence but also enhances the model's robustness against subtle spatial and visual ambiguities.
4 EXPERIMENTS
4.1 DATASETS AND PERFORMANCE EVALUATION
We validate SAGE on a diverse collection of VPR benchmarks (Tab. 1) covering common real- world challenges: Pitts30k-test and Pitts250k-test (large viewpoint variation) (Torii et al., 2013), SPED (low-quality/ high scene depth and condition changes) (Chen al., 2017), MSLS-val (multi- year urban/suburban variability) (Warburg et al., 2020). Nordland (four-season natural scenes) (Sünderhauf al., 2013), Tokyo247 (multi-view urban captures) (Torii et al., 2015), AmsterTime (historical grayscale vs. contemporary RGB) (Yildiz al., 2022), and Eynsham (rural grayscale route) (Cummins Newman, 2010; Berton et al., 2022b). Further details can be found in App. A.3.
The experiment adopts Recall @ N (R@) N) as the evaluation metric, i.e., the percentage of query im- ages for which at least one of the NN retrieved database images geographically matches the query image (within a preset threshold). Thresholds follow standard protocols: 25 meters for Pitts30k-test Pitts250k-test Tokyo24/7, Eynsham and SPED; MSLS-val uses 25 meters with azimuth within 40 degrees; Nordland uses 10 frames (Torii al., 2013; Chen et al., 2017; Warburg et al., 2020).

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Table 2:
Comparison to SoTA Methods on VPR Benchmark Datasets. The best and second best metrics are shown in red bold and blue bold, respectively. Two-stage methods are denoted by T.
SPED
Pitts30k-test
MSLS-val
Nordland
Method
Dim
R@I Q@I@@5RG
10
R@ R@S R@
R@ R@5 R@
R@
R@S R@
NetVLAD CVPR' 2016
32768
70.2 84.5 89.5
81.9 91.2 93.7
53.1 66.5 71.1
6.4 10.1 12.5
SFRS BCCV 2020
4096
80.2 92.6 95.4
89.4 94.7 95.9
69.2 80.3 83.1
16.1 23.9 28.4
CosPlace CVPR' 2022
512
75.5 87.0
89.6
88.4 94.5 95.7
82.8 89.7 92.0
58.5 73.7 79.4
MiX WACV' 2023
4096
84.7 92.3
94.4
91.5 95.5 96.3
92.7 94.6
76.2 86.9 90.3
R2Former CVPR' 2023
67.5 75.8 77.8
91.1 95.2 96.3
89.7 95.0 96.2
77.0 89.0 91.9
EigenPlaces ICCV' 2023
2048
70.2 83.5
87.5
92.5 96.8 97.6
89.1 93.8 95.0
71.2 88.1
SelaVPR ICLR' 2024
1024
83.5 92.6 94.6
90.2 96.1 97.1
87.7 95.8 96.6
72.3 89.4 94.4
SelaVPR ICLR' 2024
/
88.6 95.1 97.2
92.8 96.8 97.7
90.8 96.4 97.2
87.3 93.8 95.6
CricaVPR CVPR' 2024
4096
91.3 95.2 96.2
94.9 97.3 98.2
90.0 95.4 96.4
90.7 96.3 97.6
SALAD CVPR' 2024
8448
92.1 96.2 96.5
92.5 96.4 97.5
92.2 96.4 97.0
89.7 95.5 97.0
EDTformer TCSVT' 2025
4096
92.4 95.9 96.9
93.4 97.0 97.9
92.0 96.6 97.2
88.3 95.3 97.0
BoQ CvPR' 2024
12288
92.5 95.9 96.7
93.7 97.1 97.9
93.8 96.8 97.0
90.6 96.0 97.5
SALAD-CN CM ECCV' 2024
8448
89.5 94.9 96.1
92.6 96.8 97.8
94.2 97.2 97.4
95.6 98.6 99.1
SuperVLAD NIPS'
3072
93.2 97.0 98.0
95.0 97.4 98.2
92.2 96.6 97.4
91.0 96.4 97.7
EMVP NIPS' 2024
8448
94.6 97.5 98.4
94.0 97.5 98.2
93.9 97.3 97.6
88.7 97.3 99.3
Fol. AAAI 2025
8448
92.1 96.5 98.0
93.9 97.2 98.1
93.1 96.9 97.4
87.8 94.5 96.4
Fol. AAAI' 2025
f
92.6 96.5 97.4
94.5 97.4 98.2
93.5 96.9 97.6
92.6 96.7 97.8
2048
95.6 99.2 99.7
95.4 97.4 97.9
92.6 96.9 97.7
91.2 96.6 97.8
SAGE (Ours)
4096
97.7 99.8 100
95.6 97.7 98.3
93.7 97.3 97.8
94.4 98.2 99.0
8448
98.9 99.7 100
95.8 97.8 98.4
94.5 97.4 97.8
96.0 98.9 99.4
Table 3: Comparison to SoTA methods on more challenging datasets, Values marked with were reproduced in this work when they were not reported in the original publications.
AmsterTime
Tokyo24/7
Pitts250k-test
Eynsham
Method
Dim
R@ 1 R@s R@ 10
R@1
R@5 R 10
R@ ReIR@5Re 10
R@
R@5 R@ 10
SALAD-CM ECCV 2014
8448
57.8 77.5 81.3
96.8
97.5 97.8
95.2
99.3
91.9 95.3 96.
VuperVLAD NIPS' 2024
3072
63.9 83.9 87.3
95.6 97.8 98.1
97.3 99.4 99.7
92.1 95.6 96.4
EMVP Nups 2024
8448
86.0
90.5#
96.8* 98.1 98.7t
96.5 99.1 99.5
95.9*
96.6
FoL AAAI 2025
8448
64.6 84.3 88.2
96.2 98.7 98.7
96.5 99.1 99.5
91.7 95.3 96.2
2048
66.2 78.6 85.0
95.6 96.5 98.1
97.7 99.1 99.3
92.7 95.8 96.5
SAGE (Ours)
4096
76.0
88.0
92.3
96.5
99.1 99.4
98.2 99.4 99.5
92.9
96.0 96.8
8448 83.5 93.3 95.4
97.5 99.1 99.4
98.4 99.4 99.7
93.1 96.2 97.0
4.2
IMPLEMENTATION DETAILS
SAGE is built upon the EMVP frame- work (Qiu et al., 2024), which re- produced from its publication for a fair comparison the official code is un- available. We fine-tune two Vision Transformer backbones, ViT-B and VIT- L, which we denote as SAGE-B and The Feature Compression (Fc) and Feature Probing (Fr) branches are implemented as two- layer MLPs, reducing the feature dimen- sions to D 128 and K 64, respee- tively. The InteractHead module is im- plemented as a two-layer Transformer encoder with a model dimension of 768, 16 attention heads, and a feed forward network with dimension 1024. During training we freeze the backbone and adopt DPN for PEFT, which adaptively preserves task-specific information while greatly reducing the number of trainable parameters. Each training mini-batch is constructed with equal contribu-
Table 1: Summary of the evaluation datasets.
Number
Dataset
Description
Database
Queries
30kss3
urban, panorama urban, suburban
10,000
6,816
MSLS-val Nordland SPED
18,871
740
natural, seasonal
27,592
27,592
SAGE-L. respectively.
various scenes
607
607
Tokyo24/7 AmsterTime Pitts250k-test Eynsham
urban, time-varying
75,984
315
urban, time-related
1,231
1,231
urban, panorama
83,952
8,280
rural, historical 23,935
23,935
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tions from MSLS (all non-panoramic images from the training set) and GSV-Cities (0.56M images from 67K places). For every batch we build a new sparse geo-visual graph, and we sample P = 15 sequences from the same city and recompute cliques dynamically during training. The thresholds for this process are set to ti = 25 m and T2 -2888 x 10 Input images are resized to 224 x 224 during training and 322 x 322 during inference. All experiments are implemented in PyTTrch and run efficiently on single NVIDIA A100 GPU. We fine-tune models for 10 epochs and select the checkpoint with the highest Recall on Pitts30k-test for evaluation on the other benchmarks.
4.3 COMPARISONS WITH SOTA METHODS
In this section we provide comprehensive comparison between our proposed SAGE and range of state-of-the-art VPR methods. The comparison includes: NetVLAD (Arandjelovic et al., 2016), SFRS (Ge et al., 2020), CosPlace (Berton al., 2022a), VPR (Ali-Bey et al., 2023b), Eigen- Places (Berton et al., 2023), CricaVPR (Lu et al., 2024b), SALAD (Izquierdo & Civera, 2024), BOQ (Ali-bey et al., 2024), SALAD-CM (Izquierdo & Civera, 2025), SuperVLAD (Lu et al., 2024d), EMVP (Qiu et al., 2024), and the two re-ranking (two-stage) pipelines SelaVPR (Lu et al., 2024c) and FoL (Wang et al., 2025). Re-ranking pipelines add a computationally intensive local feature matching stage. EMVP and Fol. are the current SOTA for single-stage global retrieval and two-stage VPR pipelines, respectively; implementation details for compared methods are given in App. A.4.
As shown in Tab. 2 and Tab. 3, SAGE consistently outperforms previous methods across all bench- marks and evaluation metrics. In a higher-dimensional configuration (8448-d), SAGE reaches 94.5% @ on MSLS-val and achieves 100% @ 10 on SPED, while improving R@1 by 4.3 percentage points over the previous best single-stage method (EMVP). SAGE also maintains leading perfor- mance challenging datasets: for example, it attains 96.0% R@1 on Nordland and 83.5% R @ on AmsterTime representing substantial gains relative to FoL and EMVP. We also evaluate com- pact configurations obtained via PCA dimensionality reduction. Even under tighter budgets (2048-d and 4096-d), SAGE remains highly competitive and often matches or even surpasses recent strong baselines. For instance, the 4096-d SAGE achieves 95.6% R@I on Pitts30k-test and 97.7% R@] on SPED while preserving 100% R @ 10, demonstrating that our proposed feature amplification and epoch-wise online geo-visual sampling produce highly discriminative global descriptors. A detailed analysis of SAGE performance across varying descriptor dimensions is presented in App. A.2.
We apply t-SNE to embed spatial features from four methods into a 2D space for visual comparison. Features are extracted from 600 images at 50 locations. Fig. 3 displays the 2D data projections and the corresponding Average Intra-class Distance (AID), according to Equ. 8. SAGE-B has the smallest AID indicating the tightest within location clustering. More results is presented in Fig. 6.
Tab. 4 details parameters for various VPR methods. By employing DPN for PEFT, SAGE avoids the heavy adapter modules typ- ical of methods like SelaVPR and CricaVPR, thus achieving a markedly lower total param- eters. More strikingly, since SAGE keeps the backbone frozen and exclusively fine-tunes its lightweight DPN, SofiP, and InteractHead modules, its trainable parameters is signifi- cantly smaller than that of approaches that fine- tune portions of the Transformer encoder (e.g., SALAD, SALAD CM, and SuperVLAD). This high parameter efficiency is attained without even compromising its SOTA performance.
Table 4: Comparison of parameters (M) for vari- ous VPR methods using the DINOv2-B backbone. The value in parentheses is the number of param- eters the optional cross-image encoder.
Method
Total J Trainable & Adapter
SALAD 2014
88.0
29.8
X
SelaVPR JCLK 2024
102.8
16.2
14.2
CricaVPR CVPR' 2024
95.7 (+11.0)
9.15 (=11.0)
9.2
SALAD-CM ECCV 2014
88.0
29.8
SuperVLAD NIPS' 2004
86.6 (+11.0)
28.4
(-11.0)
X
EMVP NIPS 2024
88.5
1.96
X
SAGE (Ours)
88.5 (+7.88) 1.96 (+7.88)
Fig. 4 shows qualitative results for SAGE-B and several SOTA methods in representative challenging scenarios. SAGE consistently retrieves the correct database images, while other methods often fail to capture the most discriminative cues and produce incorrect matches. Additional qualitative visualizations are provided in App. A. as illustrated in Fig. 7. To illustrate the comparison, we show importance heatmaps produced by SoftP, SALAD, and CFP in Fig. 5. While all three highlight prominent static landmarks, but SoftP even more effectively concentrates on subtle, fine- grained, highly discriminative regions. Additional SoftP heatmaps are provided in App. A. shown in Fig. 8.
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SAGE-8 ADD 0 (0ura)
SaperOLA ALD: 0GP5 2024
EWPP-
SALAD-CM
AIc 1G4
ADD 103
2000)
2026
Figure 3: Visualization of spatial feature clustering using t-SNE for four methods and comparison of Average Intra-class Distance (AID). Numbers next each class indicate intra-class distance (ID).
Table 5: Ablation of SAGE components. All experiments use VTT-B; results reproduced in this work are marked OGC denotes Online Graph Creation and GWS denotes Greedy Weighted Sampling.
Components
SPED
Pitts30k-test
MSLS-val
Nordland
Method
Aggregation OGC GWS R@
R@5
R@
R@I
R
R@
R
R@5
R
10 R@
R@5
R@ 10
EMVP-B
CFP
91.8
96.5
97.4
93.1
96.8
97.6
93.2
96.9
97.2
80.8*
90.4
93.5
SoftP
96.8
98.2
98.7
94.6
97.2
97.9
93.6
96.8
97.1
95.2
98.4
98.7
SoftP
96.5
97.8
98.3
93.8
96.5
97.2
92.5
96.6
96.9
94.2
97.4
97.9
SAGE-B
CFP SoftP
/
/
97.5
98.4
98.9
94.9
97.3
98.0
93.9
97.1
97.4
95.4
98.5
98.8
98.0
98.7
99.2
95.4
97.6
98.3
94.3
97.2
97.6
95.8
98.7
99.2
Query
(Ours)
EMWP (NeurtPS
CheurIPS 24
SuperVLAD
SALAD CM (Eccv 24)
(a) Input Image
(b) Result SALAD
(c) Result of CFP
(d) Result of SoftP
Figure 4: Qualitative results. SAGE consistently retrieves correct database images under severe challenges.
Figure 5: Visual comparison of importance heatmaps. SoftP shows a stronger focus on fine grained regions with high discriminative value than other methods overall.
4.4 ABLATION STUDY
We ablate SAGE's components using a VIT-B backbone (8448-D) and EMVP-B as the baseline (Tab. 5). On the challenging Nordland dataset, marked by severe seasonal variations, the baseline only achieves 80.8% R 1. Integrating our SoftP and OGC modules yields substantial gains, boost- ing R@1 to 93.6% on MSLS-val val and 95.2% on Nordland. These gains suggest that SoftP enhances discriminative local feature responses, while OGC reconstructs geo-visual graph each training epoch to expose the model to evolving hard examples aligned with the current embedding space.
Adding GWS to SoftP configuration produces modest and unstable gains, suggesting sampling alone cannot exploit graph dynamics without Online Graph Creation. Enabling OGC and GWS while retaining CFP yields notable improvement, for example Pitt33k-test R @ 94.9%, but still falls short of the configuration that includes SoftP. The full SAGE configuration achieves the best results, with @ of 95.4% on 3itts30k-test and @ of 98.0% SPED, surpassing the baseline and intermediate variants. The greedy weighted clique expansion complements SoftP and OGC by focusing training the most informative clusters and enhancing descriptor discriminability.

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To evaluate the computational cost of dynamic sampling strategy, conducted an abla- tion study comparing the runtime and performance online versus offline graph creation. As shown in Table 6, our online approach incurs modest 17.7% increase in per-epoch training time. However, this modest overhead is a worthwhile investment, as translates directly to su-
Table 6: Comparison of Online and Offline Graph Creation Strategies. Runtimes are reported per epoch for the online method. For the offline strategy, mining is a one-time cost.
SPED
MSLS-ve val
Strategy
Method
Mining (min)
Train (min)
R@1
R@5
R@ 10
R@1
R@5
R@ 10
Cliquemining SAGE (wlo GWS) SAGE
4.3
25.1
90.0
95.4
96.2
94.3
96.9
97.6
Online
6.1
28.4
96.8
98.2
98.7
93.6
97.1
6.2
28.4
98.9
99.7
100
94.5
97.8
Cliquemining
21.6
25.1
89.5
94.9
96.1
94.2
97.2
97.4
Offline
SAGE (w/o GWS)
30.7
28.4
96.7
98.2
98.9
93.5
96.6
97.1
SAGE
30.9
28.4
98.5
99.3
99.5
94.2
97.3
97.7
perior accuracy, with the online SAGE model achieving higher recall on both SPED and MSLS-val. This result validates our central argument: dynami- cally adapting the sampling to the model's evolv- ing state crucial for breaking the performance bottleneck of static mining strategies. Further- more, the experiment highlights the efficiency of the GWS module itself, which adds negligible over- head while delivering a substantial accuracy boost. Crucially, this computational overhead is confined to the training phase. The inference process is un- affected, ensuring SAGE remains efficient as comparable single-stage methods at deployment.
Table 7: Convergence analysis on MSLS-val. SAGE's dynamic sampling leads to superior performance in early training epochs.
SAGE (w/ CM)
SAGE (Ours)
Epoch
R@ I R@5 R@ 10
R@I R@S R@ 10
2
92.3 96.1
96.6
92.5
97.1 96,9
96.5
4
92.7
96.6
97.0
93.4
97.4
To demonstrate SAGE's learning efficiency, tracked its early-stage training performance on MSLS-val against a baseline using the Cliquemining (CM) strategy. As shown Ta- ble 7, our model establishes a clear advantage by the fourth epoch (93.4% vs. 92.7% R@ 1). The widening performance gap confirms that our dynamic sampling strategy fosters more efficient learning, leading superior perfor- mance within the same number of epochs. As detailed in Tab. 8, ablate the InteractHead's internal dimensions. Setting dmmdee 768 and dsr 1024 yields the best performance, notably achieving 98.9% R@ on SPED. Expanding d to 1536 marginally improves Pitts30k but degrades SPED. Thus, adopt the (768, 1024) configu- ration, balancing capacity and generalization.
Table 8: Ablation on InteractHead module. We vary the model dimension (dmosee) and feed- forward dimension (dz).
SPED
Pitts30k
(dinotet, dr)
R@ R@S R@ 10 R@
R@5 R@ 10
(512, 1024) 98.8 99.5
99.7
95.5
97.6
98.2
(768, 1536)
98.6 99.3
99.7
96.0
97.9
98.4
(768, 1024) 98.9 99.7 100 95.8
97.8
98.4
5 CONCLUSION
In this paper, we presented SAGE, a unified framework that redefines Visual Place Recognition training by shifting from static sampling strategies to dynamic, slow thinking paradigm. By synergizing the lightweight Soft Probing module with InteractHead, SAGE effectively amplifies fine-grained discriminative cues and models cross-image correlations, ensuring robust feature repre- sentation even under drastic appearance variations. Crucially, our novel Online Graph Creation and Greedy Weighted Sampling mechanisms ensure that the mining process continuously synchronizes with the evolving embedding space, allowing the model to relentlessly focus the most informa- tive geo-visual neighborhoods. Extensive evaluations across eight diverse benchmarks demonstrate that SAGE establishes new SOTA, delivering exceptional retrieval accuracy while maintaining markable parameter efficiency through a frozen DINOv2 backbone. This provides a scalable and efficient foundation for future large-scale visual geo-localization systems.
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ACKNOWLEDGMENTS
This work was supported by the Beijing Natural Science Foundation (No.JQ33014), National Natu- ral Science Foundation of China (No.62271074), Taishan Scholars Program (No.TSQN202507241)) Key R&D Program of Shandong Province, China (No.205KJH2001), Shandong Provincial Uni- versity Youth Innovation and Technology Support Program (No.2022KJ29)), Shandong Provin- cial Natural Science Foundation for Young Scholars Program (No. NN..2R2050C16227 and Qilu University Technology (Shandong Academy of Sciences) Youth Outstanding Talent Program (No.2024QZJH02).
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A APPENDIX
A.1 VISUALIZATION RESULTS
23
13
-39
-39
SAGE-L
EMYP-L
-39
ALD: 0.91 (Ours)
-30
AID: 0.44
-30
AID: 0.96 (NIPS 2024)
(AAXI 2025)
14
B0Q
SALAD
CricaVPR
AID: 0.97
AID: 101
AID: 117 (CVPR
(CVPK 2024)
(CVPR 2024)
Figure 6: t-SNE visualization of feature clusters produced by SAGE-L and five other leading VPR methods. The features are extracted from 600 images across 50 distinct locations from the GSV- Cities dataset. Clustering compactness is quantitatively evaluated using the Average Intra-class Distance (AID), defined as the mean Fuclidean distance of features to their corresponding class centroid. A lower AID signifies more discriminative and compact feature representation for images of the same location. Notably, SAGE-L achieves the lowest AID (0.91), demonstrating its superior ability to group features from the same place.
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Table 9: Performance analysis SAGE across varying descriptor dimensions. Results on VPR benchmarks show consistent improvement in retrieval accuracy the dimension increases. The best and second best results for each dataset are shown in red bold and blue bold, respectively.
SPED
Pitts30k-test
MSLS-va
Nordland
Method
Dim
R@1 1
R@5
R@ 10
R@]
R@5 R@ 10
R@ 1 R@5
R@ 10
R@1
R@5
R@ 10
128
84.7
92.8
95.7
88.9
94.5 95.7
81.8 91.5
93.0
56.1
72.2
78.1
256
91.6
96.7
97.5
93.0
95.9
96.7
87.2 94.6
95.7
70.5 83.6
87.8
512
94.2
98.2
99.0
94.4 96.5
97.2
91.4 95.7
96.8
80.1 90.0
92.9
1024
95.4 99.3
99.7
95.1 97.1
97.6
92.4 96.5
96.9
87.0 94.3
96.2
SAGE (Ours)
2048
95.6 99.2
99.7
95.4 97.4
97.9
92.6 96.9
97.7
91.2 96.6
97.8
3072
96.9 99.7
99.8
95.6 97.7
98.2
92.4
97.3
97.7
93.8 97.8
98.8
4096
97.7 99.8
100
95.6
97.7
98.3
93.7
97.3
97.8
94,4
98.2 99.0
8448
98.9
99.7
100
95.8
97.8
98.4
94.5 97.4
97.8
96.0 98.9 99.4
To visually assess the quality of feature clustering, we first created a dedicated test set by select- ing 600 images from 50 distinet locations (12 images per location) within the GSV-Cities dataset, ensuring coverage of diverse scenes and conditions. We then employed t-SNE to project the high- dimensional features generated by each method into a 2D space for visualization.
For a quantitative analysis of cluster compactness, we calculated the Average Intra-class Distance (AID). This metric is derived from the Intra-class Distance (ID), which measures the mean Euclidean distance of features within a class to their shared centroid μ4, as defined by:
1 C.
ID:
Σ 11-Mill2 EEC:
AID
ID,
(8)
where O is the of feature vectors for location i. A lower AID value signifies more compact and discriminative feature representation. As illustrated in Fig. 6, SAGE-L achieves the lowest AID, which quantitatively confirms its superior ability generate robust features that are tightly elustered for the same location, effectively handling intra-class variations.
To highlight the practical robustness of our method, Fig. presents qualitative hcomparison be- tween SAGE-B and seven leading VPR methods. The visualization is structured to systematically evaluate retrieval performance across six of the most common and difficult VPR challenges: se- vere viewpoint shifts, adverse weather, drastic lighting changes, long-term temporal differences, structural alterations, and dynamic occlusions. In these demanding scenarios, most state-of-the methods falter, failing to identify the correct place and retrieving visually plausible but incorrect matches (highlighted in red). In stark contrast, SAGE-B consistently retrieves the correct database image in every case (green boxes). This demonstrates the superior resilience of our approach, which stems from its ability to learn and focus on stable, truly discriminative features while effectively mitigating the impact of significant appearance variations.
The heatmap visualizations of the SoftP module, presented in Figure 8, elucidate its underlying mechanism. A clear pattern is evident across diverse scenes where the model learns to automatically suppress features from ion-informativ or transient sources, including the sky, road surfaces, and dynamic objects such vehicles and pedestrians. Crucially, SoftP moves beyond concentrating on large static structures to prioritize fine-grained, stable details that offer reliable discriminative cues, for instance specific architectural features, window frames, and unique textures.
A.2 ADDITIONAL. RESULTS
To further demonstrate the scalability and robustness of our SAGE framework. present per- formance analysis across a range of descriptor dimensions. This analysis spans both standard and more challenging VPR benchmarks, highlighting the consistent effectiveness of SAGE.
Tab. details the performance of SAGE on four widely used VPR benchmarks (SPED, Pitts30k-ter MSLS-val, and Nordland) with deseriptor dimensions varying from 128 to 8448. The results reveal a clear and consistent trend: retrieval accuracy, measured by Recall@ systematically improves the descriptor dimension increases. Notably, even at intermediate dimension of 4096, SAGE
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Query
SAGE-B (Ours)
(NeurIPS 24)
(a) EMYP
(b) SuperVLAD (NeurIPS 24)
(c) SALAD-CM (ECOV 24)
(d) FoL (MAL 25)
CricoVER (CVP2 24)
EigenPtaces (ICCV 23)
(g) CosPlaces (CVPR 22)
Vioepoint thange
Weather change
Lightng charge
Tempora clange
Strictra
change
Dysmic
occluson
Figure 7: Qualitative comparison of SAGE-B against leading VPR methods under diverse and chal- lenging conditions. Rows correspond to challenge category, from top to bottom: viewpoint change, weather change, lighting change, temporal change, structural change, and dynamic occlusion. Cor- rect top-1 retrievals indicated by green bounding box. while incorrect matches marked in red. The results visually confirm SAGE's consistent and superior robustness across all scenarios.
Table 10: Performance of SAGE with varying descriptor dimensions more challenging datasets.
AmsterTime
Tokyo24/7
Pitts250k-test
Eynsham
Method
Dim
R@ 1 R@5
R@ 10
R@
R@5
R@10 10
R@
R@5
R@ 10
R@1
R@5
R 10
128
23.4 36.5
44.4
42.9
60.6
66.4
89.0
95.1 96.4
89.3 93.2
94.3
256
36.0
51.0
58.7
66.0
78.7
82.5
94.7
97.7
98.2
91.0
94.4
95.1
512
44.9
60.8
68.1
80.0
89.2 92.1
96.4
98.3
98.7
91.9 95.0
95.7
1024
55.6 70.0
76.9
89.5 94.6 95.9
97.3 98.9 99.2
92.4 95.5
96.2
SAGE (Ours)
2048
66.2 78.6
85.0
95.6 96.5 98.1
97.7
99.1 99.3
92.7 95.8 96.5
3072
73.6 85.8
89.9
94.9 98.4
98.7
98.1 99.3 99.5
92.9 96.0 96.7
4096
76.0 88.0
92.3
96.5 99.1 99.4
98.2
99.4 99.5
92.9
96.0 96.8
8448
83.5 93.3 95.4
97.5 99.1 99.4
98.4 99.4
99.7
93.1 96.2 97.0
achieves remarkable performance, including perfect 100% R on SPED. The results at 8448-D, such as 98.9% R@ on SPED and 96.0% R@I on Nordland, underscore the framework's 's ability to leverage higher-dimene finteer-dmmessoonal feature spaces for enhanced discriminability.
Building on this, Tab. extends the evaluation to more demanding datasets characterized by severe domain shifts, including AmsterTime (historical modern). Tokyo24/7 (extreme viewpoint and -ime-of-day changes), Pits250k-test (large-scale urban scenes), and Eynsham (rural route). The performance trend remains robust, with accuracy scaling gracefully with descriptor dimensionality.
The improvement is particularly pronounced on AmsterTime, where the R@ I score surges from 23.4% at 128-D to 83.5% at 8448-D. This demonstrates SAGE's exceptional capability
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Figure 8: Visualization of SoftP's learned feature importance. The visualizations demonstrate that SoftP automatically learns focus on stable, fine-gre ene-graieed landmarks (e.g.. building facades, struc- tural details) while effectively ignoring non-discriminative regions (sky, road) and transient objects (vehicles, pedestrians).
to handle extreme appearance variations, validating the effectiveness of our proposed dynamic geo-visual graph exploration and feature enhancement strategies. To further assess the gen- eralization capabilities of our method, we conducted experiments on the SF-small benchmark. Derived from SF-XL (Berton this dataset is particularly well-suited for evaluating generalization drawn from distinct, non-adjacent locations. The results are presented in Table 11. SAGE achieves 89.3% at R@ 1, significantly outperforming all compared state-of-the the-trteertt methods. Our method utilizes two hyper- parameters during Online Graph Creation: the geographic distance threshold r and the affinity score threshold 72- We conducted sensitivity analysis these parameters using the MSLS-val dataset, as presented in Table 12. The table also shows that the model's performance re- mains stable around these optimal values, demonstrating that our method not overly sensitive these hyperparameters and exhibits good robustness.
al., 2022a),
Table 11: Performance comparison on the SF-small benchmark.
its queries are
Method
R@1
R@5
@ 10
Crica CVPR 2024
84.5
88.7
89.2
SALAD EVPR' 2024
85.7
88.2
89.7
SuperVLAD NIPS 2014
85.8
89.1
89.5
SALAD-CM BCCV
84.0
88.0
89.8
EDTformer TCSVT 2025
87.9
89.8
90.6
EMVP NIPS
88.2
90.6
91.1
SAGE (Ours)
89.3
91.5
91.9
To further validate the effectiveness of SAGE, we present an extended comparison against sev- eral recently published SOTA methods from top-tier venues. These include VLAD-BuFF (Khaliq et al., 2024), EDTFormer (Jin et al., 2025b), and EffoVPR (Tzachor et al., 2025). The results, shown in Table 13, demonstrate that SAGE consistently outperforms these strong baselines. SAGE achieves superior performance across the majority of benchmarks. Al-
Table 12: Sensitivity analysis of T and 72-
MSLS-val
MSLS-val
R@ RØS R 10
R@ R@5 R@ 10
93.8 97.3 97.7
22775 x 10 94.1 97.6
97.8
25 94.5 97.4
97.8
2.88
x 10 94.5
97.4
97.8
93.7
97.0
97.4
3.00
10
93.9 97.3 97.6
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Query
Failure coses
Ground Truth
SAGE
(Ours)
Figure 9: Failure cases of SAGE, where the top-1 retrieval (red box) fails to match the ground truth.
though EffoVPR obtains slightly higher R@1 (11 on Tokyo24/7, it is two-stage reranking method, which introduces additional computational overhead.
Table 13: Extended comparison with recent SOTA methods on five challenging benchmarks.
Denotes two-stage reranking method.
SPED
vSl
Nordland
Tokyo24/7
Pitt2250k-tet
Method
R @ IR@5RR 1OR @ @ 1OR @ R@5R@ 10R @ IR@5R@ 10
R@5R 10
VLAD-BuFF
2ccv 24
91.4 95.9
96.9 91.8 96.0
96.2
85.1
93.8
96.0
96.2 98.7
99.4
95.5
98.5 99.2
EDTFormer 25
92.4 95.9
96.9 92.0 96.6
97.2
88.3
95.3
97.0
97.1 98.1
98.4
95.9 98.8 99.3
EffoVPR iCL:22"
93.1 97.9 98.4
92.8 97.2
97.4
95.0
98.7
98.7
98.7
SAGE (Ours)
98.9 99.7 100 94.5 97.4 97.8
96.0
98.9
99.4 97.5 99.1 99.4 98.4 99.4 99.7
Beyond the quantitative ablations, Figure presents SAGE's qualitative failure modes. These fail- ures typically occur under extreme conditions, such severe viewpoint or illumination shifts, and heavy occlusion from dynamic objects.
A.3 DATASET DETAILS
Pitts30k-test (Torii et al., 2013). The Pitts30k-test dataset is a subset the Pittsburgh 250k dataset collected from Google Street View panoramas with GPS labels. It contains urban street-view images from Pittsburgh, Pennsylvania, USA, covering diverse city environments such as roads, bridges, and buildings, with large variations in viewpoint, season, and illumination. Each location provides multiple viewpoint images, and the dataset is primarily used for evaluating VPR models in urban scenarios.
SPED (Chen et al., 2017). The SPED dataset, also known as the Specific PlacEs Dataset, consists of images captured by fixed surveillance cameras over extended time periods, covering significant changes in illumination, weather, and seasons. It contains unique locations, each with hundreds of images taken different times of day and year, providing a challenging benchmark for long-term VPR under extreme appearance variations.
MSLS-val (Warburg et al., 2020). The MSLS-val dataset is the validation split of the Mapillary Street-Level Sequences dataset, which contains street-level imagery sequences from cities world- wide captured with various devices such as smartphones, dashcams, and professional mapping cam- The validation set covers multiple cities, seasons, and weather conditions, and is widely used for tuning and evaluating VPR models under diverse geographic and environmental variations.
Nordland (Sünderhauf et al., 2013). The Nordland dataset consists of front-facing video record- ings from a train journey along the same railway route in Norway, captured in all four seasons: spring, summer, autumn, and winter. The route and camera viewpoints spatial structure while presenting extreme appearance changes due solely to seasonal and weather differences, making it valuable benchmark for cross-season VPR research.
fixed, ensuring identical
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Tokyo247 (Torii al., 2015). The Tokyo247 dataset contains urban street-view images from Tokyo, Japan, primarily sourced from Google Street View panoramas with GPS labels, and is named for including both day and night imagery to represent long-term scene changes. It features dense metropolitan areas with tall buildings as well as some open spaces, offering large variations in view- point, illumination, and dynamic objects, and is used evaluate VPR models in complex urban environments.
AmsterTime (Yildiz et al., 2022). The AmsterTime dataset is time-lapse street-view dataset of Amsterdam, containing images of the same locations captured in different years, such as 2008 and 2014, using Google Street View panoramas. It reflects long-term changes in building facades and urban infrastructure, providing a benchmark for evaluating VPR models under temporal urban trans- formations.
Pitts250k-test (Torii et al., 2013). The Pits2250k-tet dataset is the test split of the Pittsburgh 250k dataset, consisting of street-view images from Pittsburgh and surrounding areas, captured from Google Street View panoramas with GPS labels. It offers diverse urban scenes with dense road networks, buildings, and bridges, and serves as large-scale benchmark for evaluating the scalabil- ity and robustness of VPR systems.
Eynsham (Cummins Newman, 2010; Berton et al., 2022b). The Eynsham dataset GPS- synchronized street-view image sequence collected along approximately 35 km of driving routes in Eynsham, Oxfordshire, UK, using a vehicle-mounter camera. It provides continuous video frames with precise ground-truth positions, making it suitable for sequence-basee place recognition and loop closure detection experiments.
SF-XL (Berton et al., 2022a). The San Francisco eXtra Large (SF-XL) dataset is massive and dense new benchmark designed to push visual geo-localization research towards realistic, city-wide applications. Comprising over 41 million images captured across a decade, it presents significant real-world challenges such as long-term temporal variations and a domain shift between its Street View database and crowd-sourced queries.
A.4 COMPARED METHODS DETAILS
NetVLAD (Arandjelovic et al., 2016)'. A classic VPR method with learnable VLAD layer plug- gable into any CNN. Uses VGG-16 backbone, trained on Pitts30k, optimized via weakly supervised ranking loss, outperforming traditional methods.
SERS (Ge et al., 2020) Addresses GPS noise by mining hard positives via self-superviedd fine- grained region similarities, multi- multi-generation training. Based on NetVLAD, VGG-16 backbone, trained on Pitts30k, outperforming
then.
CosPlace (Berton et al., 2022a) Solves scalability in large-scale localization by framing training as classification. Constructs SF-XL dataset, uses CosPlace Groups VGG-16/ResNet backbone, outputs 512D descriptors, low memory usage, suitable for city-scale applications.
MixVPR (Ali-Bey et al., 2023by*. This is a novel holistic feature aggregation method for VPR. takes feature maps from pre-trained backbones as global features and iteratively incorporates global relationships into each feature map through stacked Feature-Mixer blocks (composed solely of multi-layer perceptrons), without the need for local or pyramidal aggregation. Using backbones like ResNet and trained on datasets such as GSV-Cities, it outperforms existing methods by large margin with less than half the number of parameters compared to CosPlace and NetVLAD.
R2Former
(Zhu et al., 2023)3. This method introduces R2Former, unified framework that employs a pure Transformer architecture to handle both global retrieval and local reranking in a single, end- to-end model. Its novel reranking module replaces slow geometric verification with learnable Transformer that analyzes richer cues like feature correlation and attention, achieving state-of-the- art accuracy while dramatically reducing inference time and memory usage.
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EigenPlaces (Berton et al., 2023) This method embeds viewpoint robustness into learned global descriptors by clustering training data to explicitly present the model with different views of the same points of interest, without extra supervision. Using backbones like VGG-16 or ResNet with GeM pooling and trained on the SF-XI dataset, outperforms state-of-the-art methods on most datasets, requiring 60% less GPU memory for training and using 50% smaller descriptors.
SelaVPR (Lu et al., 2024c)'. It proposes a hybrid global-local adaptation method that adapts pre-trained foundation models (e.g., DINOv2) via lightweight adapters without modifying the pre- trained model parameters, efficiently generating global features for candidate retrieval and local features for re-ranking. It also introduces a mutual nearest neighbor local feature loss to avoid time- consuming spatial verification. Outperforming state-of-the-ar methods on benchmarks like MSLS, consumes only about 3% of the retrieval time of RANSAC-based two-stage methods.
CricaVPR (Lu et al., 2024b) It proposes cross-image correlation-awan representation learning, using attention to correlate features of multiple images batch, enabling each image feature to gain useful information from others for enhanced robustness. A multi-scale convolution-enhanced adaptation method is designed to insert lightweight adapters into frozen pre-trained foundation mod- els (e.g., DINOv2) to introduce multi-scale local information. It outperforms state-of-the-art meth- ods by large margin on multiple benchmarks with shorter training time.
SALAD (Izquierdo & Civera, 2024).. It reformulates NetVLAD's soft assignment of local features to clusters as an optimal transport problem, considering both feature-to-cluster and tlustcr-to-feature relations, and introduces 'dustbin' cluster to discard non-informative features (Xu et al., 2024). Using DINOv2 as the backbone with fine-tuning, trains in only 4 epochs. This single-stage method outperforms both single-stage and two-stage methods, with fast inference speed.
BoQ (Ali-bey et al., 2024)I0. This method learns set of global queries and uses cross-attention probe input features for consistent information aggregation. It supports both CNN and Vision Transformer backbones, trained on the GSV-Cities dataset. As one-stage global retrieval method without re-ranking, surpasses two-stage methods, and is fast and efficient.
SALAD-CM (Izquierdo & Civera, 2025)" (225)".This This work addresses the insufficient Geographic Dis- tance Sensitivity (GDS) of existing VPR models by proposing a novel sample mining strategy. constructs a graph of visually similar images and samples cliques (sets of geographically close im- range
ages) from the graph as training batches enhance the model's ability to distinguish geographic distances. Based on models like DINOv2 SALAD and MixVPR, trained on densely sampled datasets such as MSLS and Nordland, significantly improves recall without increasing inference computational overhead.
VuperVLAD (Lu et al., 2 This method improves NetVLAD by removing cluster centers and using a small number of clusters, enhancing cross-domain generalization and simplifying the model. also proposes I-Cluster VLAD, which generates extremely low- dimenniioon descriptors by intro- ducing "ghost clusters" and outperforms methods like GeM pooling with the same dimension. Using Transformer backbones (e.g.. DINOv2) and trained on datasets like Pitts30k, it outperforms existing methods with lower feature dimensions.
EMVP (Qiu et al., 2024)13_ This method leverages Visual Foundation Models(e.g.. DINOv2) and proposes Centroid-Free Probing (CFP) stage that second-order features better adapt VFM descriptors. It introduces Dynamic Power Normalization (DPN) module to adaptively preserve task-specific information in both recalibration and CFP stages, forming Parameter Efficiency Fine- Tuning (PEFT) pipeline. It achieves excellent performance on datasets like MSLS and Pitts250k, saving 64.3% trainable parameters compared existing state-of-the-an art PEFT methods.
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FoL (Wang al., 2025)|4. This two-stage VPR method models reliable discriminative regions via AxtractionAAggrggaln Spatial Alignment Loss (SAL) and Foreground -aruuududud Contrast En- hancement Loss (CEL), guiding global feature generation and efficient re-ranking, It introduces weakly supervised local feature training strategy based on pseudb-correspondnnce and a discrim- inative region-guidee efficient re-ranking pipeline. Using DINOv2 the backbone and trained on GSV-Cities, it outperforms existing two-stage methods on multiple benchmarks with higher compu- tational efficiency.
VLAD-BuFF (Khaliq et al., 2024) This method improves visual place recognition accuracy by implementing a burst-aware weighting mechanism that discounts repetitive features emphasize more distinctive visual cues. Simultaneously, it achieves high computational efficiency by using pre-projection layer for local features, initialized with PCA, which enables rapid aggregation lower-dimensional space while maintaining high recall.
EDTFormer (Jin et al., 2025b) This method introduces EDTformer, simplified transformer decoder architecture that utilizes set of learnable queries to efficiently decode and aggregate crucial information from image features for robust place recognition. Furthermore, enhances the DINOv2 backbone with novel Low-rank Parallel Adaptation (LoPA) method, which enables highly memory and parameter-efficee efffeccenttff fine-tuning to deliver high performance with lower training costs.
EffoVPR (Tzachor et al., 2025). This method introduces EffoVPR, which effectively utilizes foundation model by extracting powerful local descriptors from its internal self-attention layers for highly effective re-ranking process, in zero-shot setting. Furthermore, its single-stage approach achieves of-te-of-the-ar performance with exceptionally compact global features by sim- plifying training to fine-tune only the model's final layers, thus eliminating the need for external aggregation modules.
A.5 LIMITATIONS & DISCUSSIONS & FUTURE WORK
Limitations and Discussions.
While achieving strong performance, we identify a few areas for further exploration. First, navi- gating highly dynamic scenes with rapid occlusions (e.g., as shown Fig. 9) remains an ongoing challenge, as reliable background cues may be temporarily obscured. Second, while the per-epoch overhead of Online Graph Creation is entirely acceptable on current benchmarks, scaling to ex- traordinarily large datasets might incur increased computational demands, representing conscious balance between efficiency and adaptability. Third, our graph construction benefits from reason- ably accurate geographic coordinates. In scenarios with highly noisy sparse GPS data, learning granular spatial distinctions may require further robust adaptations.
Future Work.
First, to better handle dynamic scenes, future work could integrate foundation models like SAM and CLIP to explicitly mask transient objects, encouraging the model to focus stable background contexts. Additionally, exploring multi-view 3D representations offers a potential direction. By reasoning across multiple viewpoints and aggregating geometry-grounded features, the framework could capture richer spatial structures to enhance generalization in complex environments.
Second, to enhance robustness against extreme domain shifts (e.g., historical vs. modern images), we plan to incorporate broader multimodal cues (Ou et al., 2025; Xu et al., 2025), Fusing visual fea- tures with textual metadata, 3D contextual reasoning. or topological graph structures could provide complementary and rich semantic signals for more resilient matching across varying domains.
Finally, the core principle of adaptive graph-based sampling holds promise beyond VPR. Applying this dynamic, "slow thinking" paradigm to other deep metric learning tasks, such as person re- identification fine-grained image retrieval, could fruitful area of research.
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22


Reviews:
[{"review_id": "m95KlBQsfq", "reviewer": "Reviewer_oNFe", "summary": "This paper proposes SAGE, a unified training framework for Visual Place Recognition that enhances spatial-visual discrimination through dynamic graph-based sample mining and lightweight feature enhancement modules. The method introduces Soft Probing for adaptive patch weighting and an online geo-visual graph to continuously update hard samples during training. Extensive experiments demonstrate that SAGE achieves state-of-the-art performance across eight challenging VPR benchmarks with high parameter efficiency, using a frozen DINOv2 backbone and parameter-efficient fine-tuning.", "questions": "Please see weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The experimental results are exceptionally strong, demonstrating state-of-the-art or highly competitive performance across a diverse set of eight benchmarks, including challenging datasets like SPED, Nordland, and AmsterTime that test robustness to seasonal, illumination, viewpoint, and long-term temporal changes.\n\n2. The proposed dynamic geo-visual graph mining strategy is a principled and well-motivated approach to hard sample mining. By reconstructing the graph each epoch, it effectively addresses a key limitation of static sampling strategies and ensures the training focuses on currently relevant challenging examples.\n\n3. The Soft Probing module and InteractHead are lightweight yet highly effective, enhancing local feature discriminability and cross-image coherence with a negligible number of added parameters, showcasing an excellent performance-to-cost design.\n\n4. The framework is highly efficient, achieving its top-tier performance by freezing the DINOv2 backbone and employing parameter-efficient fine-tuning, resulting in a significantly lower number of trainable parameters compared to many existing methods, which is a significant practical advantage.", "weaknesses": "1. While the per-epoch graph reconstruction overhead is noted to be marginal, it could become a non-trivial computational bottleneck when scaling to extremely large-scale datasets, and a more in-depth scalability analysis would be beneficial.\n2. This paper only provides results on the MSLS-val dataset and lacks results on the MSLS-challenge dataset. Since the labels for MSLS-challenge are not publicly available and online testing is required, results on this dataset would be more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6isShCmmpj", "reviewer": "Reviewer_NcXJ", "summary": "This paper introduces SAGE , a new framework for VPR that addresses several challenges in large-scale geotagged image retrieval. VPR is essential for tasks such as autonomous robot navigation, loop closure detection in autonomous driving, and map construction. Previous methods have focused on fine-tuning descriptors or static sampling strategies but have failed to account for the dynamic interplay between spatial context and visual similarity during training. SAGE improves VPR performance by dynamically constructing an online geo-visual graph, enhancing local feature aggregation, and using hard sample mining.", "questions": "1. The writing is not clear enough and fails to highlight the key contributions of this paper.\n2. While the paper introduces the SAGE framework and its various components (such as SoftP, InteractHead, and the dynamic geo-visual graph mining), the justification for why these specific techniques were chosen over others is not sufficiently detailed. For instance, the paper does not provide a detailed comparison of SAGE’s components with other potential alternatives that might achieve similar outcomes.\n3. The paper may benefit from a deeper analysis of why SoftP, InteractHead, and other components were selected. A discussion on related work and the performance of other possible methods in a similar context could enhance the novelty of the approach. For example, why are these methods preferable over existing techniques like spatial attention, or other forms of feature aggregation?\n4. There is a mention of a performance drop in highly dynamic environments, but this is not sufficiently examined.\n5. Please include more comparisons with the latest state-of-the-art methods.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. SAGE introduces a dynamic approach to hard sample mining and geo-visual graph construction, addressing limitations in previous methods.\n2. SAGE achieves top performance on multiple VPR benchmarks, demonstrating its effectiveness across various environmental conditions, such as viewpoint shifts, weather changes, and lighting variations.", "weaknesses": "1. The writing is not clear enough and fails to highlight the key contributions of this paper.\n2. While the paper introduces the SAGE framework and its various components (such as SoftP, InteractHead, and the dynamic geo-visual graph mining), the justification for why these specific techniques were chosen over others is not sufficiently detailed. For instance, the paper does not provide a detailed comparison of SAGE’s components with other potential alternatives that might achieve similar outcomes.\n3. The paper may benefit from a deeper analysis of why SoftP, InteractHead, and other components were selected. A discussion on related work and the performance of other possible methods in a similar context could enhance the novelty of the approach. For example, why are these methods preferable over existing techniques like spatial attention, or other forms of feature aggregation?\n4. There is a mention of a performance drop in highly dynamic environments, but this is not sufficiently examined.\n5. Please include more comparisons with the latest state-of-the-art methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oakiwnG20Q", "reviewer": "Reviewer_AM9N", "summary": "The paper presents a new model for Visual Place Recognition (VPR), which adds many different improvements on top of existing methods. Results show consistent improvements over existing methods, although it is not clear if the evaluation settings are fair towards other VPR methods.", "questions": "See weaknesses above", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "Good results\nThorough evaluation against other methods", "weaknesses": "1. Some text is obviously hallucinated. SPED stands for Specific PlacEs Dataset, not Street View Place Recognition Dataset (line 894). Tokyo247 is called 24/7 because it has night imagery (24 hours), not because it contains approximately 247 km, as written in line 909.\n\n2. There are hallucination-like unsubstantiated claims throughout the paper, some of which are quite arguable at best, like\n- line 052: \"However, approaches driven by VFMs or learned queries often demand careful regularization and substantial resources in terms of computation and data to ensure stable fine-tuning.\" What is this referring to? I'm not aware of any paper supporting this claim, and models like SALAD and BoQ (DINOv2-based) do not demand careful regularization and substantial resources, but quite the opposite.\n- line 078: \"This renders them less effective in scenarios that demand awareness of spatial context and the evolving appearance of hard samples\". This is simply false, methods based on VFMs are more effective in every scenario, in fact SOTA VPR methods since 2024 all rely on VFMs, as proved by literally all methods on table 3.\n- line 320: \"Since the EMVP repository is actively maintained\". EMVP codebase is unavailable, and the link indicated by EMVP authors returns 404 (https://github.com/vincentqqb/EMVP)\n\n3. The method is overall quite hard to understand, which makes it difficult to evaluate its soundness and novelty. Figure 2 does not help in understanding. For example, line 241 suggests that B images form a sequence, and the sequence is processed as one, meaning that the descriptor of an image are influence by the descriptors of other images in the batch. It is not clear if this happening also at test time? If yes, this is a significant (unfair) advantage against other baselines, because it relies on the locality of queries (as consecutive queries in many datasets come from the same areas), whereas other methods aim to localize queries individually.\n\n4. The paper would be significantly stronger if results were also shown on SF-XL, which is the only city-wide VPR dataset to date. Also, SF-XL (or even SF-small) does not have consecutive / nearby queries so it would lift doubts about evaluation fairness.\n\n5. Line 112 is contradictory: what does it mean \"tuning a frozen DINOv2 backbone\"? Is it tuned or frozen? Later on PEFT is mentioned making it look like the actual DINOv2 backbone is frozen but LoRA is used\n\n6. PEFT is not cited anywhere. PEFT should be cited, as well as the LoRA paper if a LoRA is used.\n\n7. The OGC seems very similar to SALAD-CM graph, with the only difference that SALAD-CM creates it only once while OGC is created multiple times. Are there any other differences?\n\n8. The ablation is incomplete. Given that the paper mentions that online graph creation is necessary, while previous methods compute it offline (like SALAD-CM using SALAD to compute graph only once at the beginning) the ablation should present results with offline graph creation as well.\n\n9. The value of the threshold τ (line 259) is never defined.\n\n10. The paper claims that Dynamic Geo-Visual Graph Mining leads to faster convergence (line 107 and line 293), but this is not proven nor discussed anywhere.\n\n11. Eynsham is a dataset created in the paper \"Deep Visual Geo-localization Benchmark\", which must be cited, using raw data by Cummins & Newman. Also, Eynsham uses a 25 threshold, not 5 meters (line 315).\n\n12. OGC is sometimes defined as Online Graph Creation and sometimes as Online Graph Construction (minor weakness).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ThnWlHNt1d", "reviewer": "Reviewer_EQqS", "summary": "This paper proposes SAGE (Spatial-Visual Adaptive Graph Exploration), a novel training framework for Visual Place Recognition (VPR) that dynamically integrates spatial and visual cues through online graph construction and adaptive sampling.\nUnlike prior methods that use static sampling or fixed descriptor aggregation, SAGE introduces:\n1. Soft Probing (SoftP): a lightweight, residual reweighting module that amplifies discriminative local patches before aggregation;\n2. InteractHead: a cross-image attention block improving descriptor coherence across views;\n3. Dynamic Geo-Visual Graph (OGC): rebuilt each epoch to reflect evolving embeddings;\n4. Greedy Weighted Sampling (GWS): an adaptive clique-expansion strategy for efficient hard sample mining.", "questions": "None.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Novel Training Paradigm: The idea of reconstructing an online geo-visual graph per epoch is elegant and well-justified. It aligns with the dynamic nature of embedding spaces and resolves the “stale hard samples” issue common in previous static mining methods.\n2. Effective Feature Enhancement: The SoftP module is a simple yet powerful mechanism that improves local discriminability with negligible computational overhead. The InteractHead effectively models cross-view consistency, leading to more coherent global descriptors.\n3. Comprehensive Empirical Validation: Results across eight datasets convincingly show SAGE’s superiority over strong baselines such as EMVP (NeurIPS’24), FoL (AAAI’25), and SALAD-CM (ECCV’24). The method also performs remarkably well with compact descriptors (4096-D), highlighting scalability.\n4. Parameter Efficiency: Freezing DINOv2 while introducing only lightweight trainable modules (SoftP, DPN, InteractHead) demonstrates impressive efficiency—achieving SOTA results with fewer parameters than most competing methods.\n5. Clear Presentation and Strong Technical Depth: The paper is well-written, logically organized, and provides strong ablation and visualization support (e.g., t-SNE clustering, SoftP heatmaps, AID analysis).", "weaknesses": "Although efficient in parameters, the online graph construction and clique expansion may increase runtime. A small ablation or runtime comparison would strengthen the claim of overall efficiency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)