EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing
Authors: Qibo Qiu
Shun Zhang
Haiming Gao
Honghui Yang
Haochao Ying
Wenxiao Wang
Xiaofei He
Venue: NeurIPS 2024 poster (2024)
Keywords: Visual Foundation Model
Visual Place Recognition
Parameter Efficiency Fine-Tuning
Primary area: robotics
TL;DR: This paper proposes a novel and effective Parameter Efficiency Fine-Tuning (PEFT) pipeline of adapting a visual foundation model in the visual place recognition task.

Abstract: Visual Place Recognition (VPR) is essential for mobile robots as it enables them to retrieve images from a database closest to their current location. The progress of Visual Foundation Models (VFMs) has significantly advanced VPR by capturing representative descriptors in images. However, existing fine-tuning efforts for VFMs often overlook the crucial role of probing in effectively adapting these descriptors for improved image representation. In this paper, we propose the Centroid-Free Probing (CFP) stage, making novel use of second-order features for more effective use of descriptors from VFMs. Moreover, to control the preservation of task-specific information adaptively based on the context of the VPR, we introduce the Dynamic Power Normalization (DPN) module in both the recalibration and CFP stages, forming a novel Parameter Efficiency Fine-Tuning (PEFT) pipeline (EMVP) tailored for the VPR task. Extensive experiments demonstrate the superiority of the proposed CFP over existing probing methods. Moreover, the EMVP pipeline can further enhance fine-tuning performance in terms of accuracy and efficiency. Specifically, it achieves 93.9\%, 96.5\%, and 94.6\% Recall@1 on the MSLS Validation, Pitts250k-test, and SPED datasets, respectively, while saving 64.3\% of trainable parameters compared with the existing SOTA PEFT method.

EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing
Qibo Qiu Shun Zhang Haiming Gao
Honghui Yang! Haochao Ying4. Wenxiao Wang Xiaofei He*
State Key Lab of CAD&CG, Zhejiang University
2 China Mobile (Zhejiang) Research & Innovation Institute, "Zhejiang Lab "School of Public Health and Second Affiliated Hospital, Zhejiang University School of Medicine School of Software Technology, Zhejiang University qiuqibo_zjuee zjušzju. edu. en, haochaoyingāzju edu.cn
Abstract
Visual Place Recognition (VPR) is essential for mobile robots as it enables them retrieve images from a database closest their current location. The progress of Visual Foundation Models (VFMs) has significantly advanced VPR by cap- turing representative descriptors in images. However, existing fine-tuning efforts for VFMs often overlook the crucial role of probing in effectively adapting these descriptors for improved image representation. Centroid-Free Probing (CFP) stage, making novel use of second-order features for more effective use of descriptors from VFMs. Moreover, to control the preserva- tion of task- information adaptively based on the context of the VPR, we introduce the Dynamic Power Normalization (DPN) module in both the recalibra- tion and CFP stages, forming novel Parameter Efficiency Fine- Temn-Tunigg (PEFT) pipeline (EMVP) tailored for the VPR task. Extensive experiments demonstrate the superiority of the proposed CFP over existing probing methods. Moreover, the EMVP pipeline can further enhance fine-tuning performance in terms accuracy and efficiency. Specifically, achieves 93.9%, 96.5%, and 94.6% Recall @ the MSLS Validation, Pits250k-ttest, and SPED datasets, respectively, while saving 64.3% of trainable parameters compared with the existing SOTA PEFT method. The code is available at https:///// //g////i //gtttb....////////// wtt./ooomcioeeeeeeeeeeeeeeeeeeee http//iinnnnn///////
this paper, we propose the
Introduction
Visual Place Recognition (VPR) is indispensable for mobile robots and autonomous vehicles, enabling key functions such as global localization [1]. Simultaneous Localization and Mapping (SLAM) [2), and scene understanding [3). VPR is often tackled as image retrieval problem, where the objective to match the query (an image representing the current location) with images from previously visited places. To achieve efficient matching, typically based distance metrics like Euclidean distance, the VPR system aggregates the local descriptors of each image into a global descriptor. However, VPR faces unique challenges compared conventional image retrieval tasks, including drastic changes in image perspectives, seasonal variations, and occlusions. Consequently, many researchers are dedicated exploring robust local descriptors that exhibit invariance these challenges. The advent of deep learning significantly brings VPR to a new stage characterized by enhanced robustness and improved accuracy [4: 5; 7: 8; 10; 11].
Despite the carefully designed pipelines, these methods typically involve training a model from scratch environment-specife data. However, the diversity of application environments making
"Equal contribution. Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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A7
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(a) LP
(b) GeM Pooling
(c) NetVLAD
(d) CFP (Ours)
Figure I: Comparison of different probing methods. (a) The most popular Linear Probing (LP) in classification fine-tuning. (b) Generalized-Mean (GeM) pooling adapted by SelaVPR [13], which can be seen as generalized form of first-order feature. (c) The NetVLAD operation simplified by SALAD [12]. (d) The proposed Centroid- Probing (CFP) which provides theoretical and empirical justification for this simplification, fixing interpretability and performance issues that were present otherwise.
it challenging to collect sufficient data across different settings. To address this, a few recent studies [12; 13] have explored the potential of the Visual Foundation Model (VFM). These studies mainly focus on designing effective adapters within the backbone while only employing existing aggregation (probing) methods. However, specific probing techniques for more effective fine-tuming in the VPR task remain largely untapped. Typically, the most popular probing techniques mainly exploit first-order statistics of features, including Linear Probing (LP) for classification and Generalized- Mean (GeM) pooling for VPR, as shown in Figure (a) and (b). However, second-order statistics have been well-proven important for fine- fine-grained classification tasks [14], but LP and GeM are unable to capture them explicitly.
To this end, we revisit the classic method NetVLAD [4] as bilinear pooling (i.e., the most basic second- order statistics), which aggregates local descriptors into a global one for fine-graine one-grained classification. However, NetDLAD requires a costly offline initialization of the semantic centroids, limiting its flexibility for fine-tuning on different datasets. In addition, inaccurate centroids can introduce inductive bias (for instance, initializing centroids in urban scenes but training or inferring in rural scenes), and affect the model's generalization ability. On the other hand, semantic centroids serve as priors for probing (aggregation), and simply removing them, as shown in Figure (c), can lead to decline in aggregation performance (detailed in Section 3.1). Delightfully, we observe that the explicit calculation of semantic centroids can be avoided when introducing simple and effective Constant Normalization (CN) (detailed Section 3.2). On this basis, a novel Centroid- Free Probing (CFP) stage is naturally introduced, which takes efforts of the second-order statistics of features.
Furthermore, due to the fact that VPR heavily relies on small overlapping regions between different images to make judgments when dealing with changes in image perspectives, preserving information from these discriminative regions crucial. We design the Dynamic Power Normalization (DPN) module to adaptively control the preservation of task-specific information during the CFP stage, referred to as DPN Moreover, due to the different training objectives, the general representation capability of pre-trained VFMs tends to focus more foreground objects. However, the VPR task relies more on background regions such as the salient building. To address this, we insert DPN modules into the backbone, termed DPNR. to enhance the preservation of information from these key background regions. With the backbone frozen, trainable DPNR, modules adaptively control the preservation of task-specific background information in intermediate features, effectively contributing to Parameter Efficiency Fine-Tuning (PEFT). In the remainder of this paper. it is termed the recalibration stage. Thus, we propose novel PEFT pipeline named EMVP by combining both recalibration and CFP stages. The main contributions of this paper can be summarized as follows:
We are among the first to explore probing techniques for VPR tasks. By discussing the classical NetVL.AD aggregation method, we demonstrate that avoiding costly and unstable initialization of semantic centroids allows for more effective fine-tuning of VFMs.
We propose the novel DPN module to adaptively control the preservation of task-specific informa- tion both recalibration and CFP stages. Based on the above, more effective PEFT pipeline, named EMVP, is proposed.
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Extensive experiments demonstrate that the proposed EMVP pipeline significantly contributes to the more accurate VPR. Plenty of ablation studies have verified the effectiveness of indispensable components (i.e., CFP, CN, DPNc, and DPNR).
2 Related Work
Visual Place Recognition (VPR) is typically studied as an image retrieval problem. Based on research topics, advanced VPR approaches can be categorized into improvements on the feature extraction of the backbone network [10], aggregation methods for local descriptors [4; 15; 16]. the design of metric loss functions [17: 71. investigations into robustness against viewpoint changes [6; 18; 19], and others. This section discusses researches closely related to the proposed method.
2.1 Aggregation in Visual Place Recognition
Traditional VPR methods typically employ
[20; 21; 22]. Vector of Locally Aggregated Descriptors (VLAD) [23; 24; 25] or Fisher Vectors (FV) [26; 27; 28) to aggregate local features such SIFT [29] and SURF [30] into global ones. However, traditional methods for hand-crafted feature extraction are not data-driven. With the increase in data volume, these methods suffer from insufficient generalization and robustness. To address this problem, Arandjelovic et al. [4] proposed an end-to-end trainable generalized VLAD layer, NetVLAD, which greatly promotes the aggregation of features extracted from deep learning methods. Therefore, with the development of deep learning, NetVLAD becomes the most popular aggregation method for the VPR task [6; 8; 5J. Alternative techniques to NetVLAD include average/max pooling, R-MAC [31], and Generalized Mean (GeM) [15; 32]. Although these methods exhibit promising effectiveness in retrieving images, they regularly demonstrate inferior performance compared NetVLAD the VPR task [16: 33]. Benefiting from the emergence of the Visual Foundation Model (VFM) and embodied AI, mobile robots have progressed greatly in visual tasks. For instance, the latest research [34] shows that the combination of a self-supervised pre-trained ViT model (i.e., DINOv2) and the unsupervised aggregation method VLAD exhibits robust zero-shot VPR performance. This motivates us to explore the cooperation between supervised NetVLAD and VFM for better accuracy.
In this paper, we avoid the costly explicit calculation of semantic centroids required by Ne(VLAD, by introducing a simple and effective Constant Normalization (CN) (detailed in Section 3.2). On this basis, a novel Centroid-Free Probing (CFP) stage including the DPNc module proposed to employ second-order features when fine-tuning VFM for better VPR performance.
Differences between CFP and SALAD [12]. SALAD also avoids explicit calculation of the semantic centroids when full fine-tuning a VFM, directly aggregating local descriptors with a summation. More impressively, show that CFP admits theoretical and empirical justification for this simplification, fixing interpretability and performance issues that were present otherwise. Furthermore, a novel PEFT pipeline (i.e., EMVP) tailored for the VPR task is proposed, innovatively employing the same DPN module in both recalibration and CFP stages for task-specific information preservation. Therefore, superior performance can be achieved with minimized trainable parameters.
2.2 Fine-tuning of Visual Foundation Model
Inspired by the remarkable language generation capabilities and interactivity demonstrated by the GPT series [35], pre-trained on expansive text corpora, subsequent researches in VFMs has flourished. VFMs, including SAM [36], DINOV2 [37], and the multi-modal CLIIP [38], exhibit notable visual generalizability and robustness in the realm of 2D image recognition. Accordingly, there are also plenty of researches that focus on fine-tuning these VFMs in diverse downstream tasks, includ- ing adapters [39], prompt tuning [40], Low-Rank Adaptation (LoRA) [41], etc. Nonetheless, the mainstream efforts of PEFT primarily aim to improve parameter efficiency and mitigate overfitting, neglecting to learn a dusk-dependent classification head. As result, efforts of tuning a linear classifier rely solely on the first-order features, i.e., LP, suffering from inferior performance. To address this, Gao el al. [42] propose a novel Moment Probing (MP) method, which firstly leverages the second-order features that are rich in statistical information for PEFT. The second-order features used in MP are the covariance representation of the first-order features extracted by a single branch. In contrast, the second-order covariance matrix used in the proposed CFP stage comes from first-order
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Recalibration
Centroid-Free Probing (CFP).
X.V
Fc
LayersNorm
Patchify
MIUJA
MLP
DPN
g
DPN
flatten
R
Fp
CN
Trainable
Frozen
Element-wise Addition
Matrix Multiplication
Figure 2: Overall pipeline of the proposed EMVP, including recalibration and CFP stages. Feature matrices from the two branches (i.e., FC and Fp) are multiplied to obtain fine-grained features for the improved VPR performance. The Dynamic Power Normalization (DPN) layer can be inserted into both the recalibration and CFP stages to enhance the task-specific fine-tuning performance.
features extracted by two different branches. Note that both CFP and MP make use of second features, but they have different theoretical bases and excel in different tasks.
3 Method
The overall EMVP pipeline is illustrated in Figure 2, which can be further divided into the recalibration and Centroid-Free Probing (CFP) stages. The idea of CFP originates from NetVLAD and facilitates a more effective adaptation of VFMs to VPR tasks, with details elaborated in Section 3.1 and 3.2. Additionally, we propose the Dynamic Power Normalization (DPN) module in Section 3.3, and incorporate it into both the recalibration and CFP stages to enhance fine-tuning performance, thereby making the features extracted by the backbone network more task-specific.
3.1 Preliminaries
Global aggregation crucial for robust and accurate visual place recognition, which aggregates local descriptors into fixed-size global one. NetVLAD [4] is one of the most popular global aggregations based on the bag-of-visual-woor for each semantic centroid, which represents the sum of distances from the semantic centroid to the assigned local descriptors as follows:
[20] theory. Specifically, NetVLAD produces statistic embedding
VA = Σ Px: - Ck),
where C2 and V& represent the embedding and the statistic embedding of the k-th semantic centroid, respectively. L denotes the number of local descriptors in an image, and X; represents the i-th local descriptor. K is the number of total semantic centroids. Pit indicates the probability that X, is assigned to C₁, which can be predicted by linear layers. The calculation of global descriptor can be summarized as:
g = NeeVLAD(XX
0),
where X' [X1, X....., Xr.}
RLLM represents all local descriptors from an image. G is the global descriptor for visual place recognition, which is obtained by concatenating V {V1, V2...., VK} E xxxM
Note that the semantic centroids C = (C,,C,....,, E KKxM are also trainable, and the initialization of C depends on an offline clustering process: Initially, an off-the-shelf backbone model is used to extract local descriptors from each image, involving costly iteration through pre-collected image set. Subsequently. k-means clustering applied to these descriptors to obtain the semantic centroids C. However, the offline clustering process overlooks an issue: both the pre-collected image set and off-the-shelf backbone model should compatible with the training set and trained VPR model. In other words, the fine-tuning performance is sensitive to the initialization process [34]. Therefore, it motivates us to explore methods to avoid the explicitly computation the semantic centroids and further stimulate the potential of VFM in the field of VPR.

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3.2
Centroid-Free Probing
As discussed by [43], the NetVLAD operation can be written as bilinear pooling model. Specifically, different from the typical centroid- -wie calculation pipeline (i.e., Equation and 2), it can be calculated by accumulating local-wise deseriptors as follows:
G NetV LAD(X,C)
Σ - - -
Ck| Pit.Pit....P Pil....Pii P.kk P.K. P..... ...kl)
where P.
[ P:...... P:k] and O indicates element-wise product. Note that Equation 3 con- tains the semantic centroids C, which indicates that the corresponding costly and unstable offline initialization discussed in Section 3.1 is still needed.
We observed that since semantic centroids are shared when extracting global descriptors for different images, if the value of Σ P. can be guaranteed to be constant, then the term after the minus sign in Equation 4 can be treated as negligible constant term. Thus, the explicit calculation of C can be avoided during the training process. In this paper, we resort to post normalization methods (e.g.. softmax and l2 normalization) to constrain the value of Σ P: to be constant, which is referred as Constant Normalization (CN) hereafter. The above simplification is formulated follows:
g =
Σ x x P. - C2:....... Ckl O
Σ P.) expannd D).
constont
Therefore, represent the global descriptor follows, by omitting the constant term:
Σ x x P; xx== Fo(X)"F((a))
(5)
where the probability matrix P P2.... PL}} E BLLK represents the probability from L, local descriptors K semantic centroids. Note that, local descriptors X' are fed to linear layers Fc and Fp, which are designed for dimensionality reduction and computation of probability matrix P. respectively. the context of fine-tuning VFMs, we refer to this simplified aggregation operation as Centroid-Free Probing (CFP), as illustrated in Figure 2. It implicitly leverages the priors brought by centroids through bilinear design, while avoiding explicit centroid initialization and training.
3.3 Dynamic Power Normalization in CFP and Recalibration
As VPR heavily relies small overlapping regions handle perspective changes, preserving information from these discriminative regions essential. this paper, we resort to post-processing methods preserve task-specific information from these regions, thereby enhancing the robustness of second-order features. Extensive advanced studies [44; 14; 45] have verified that using the Matrix Power Normalization (MPN) method for feature post-processing after bilinear pooling can significantly improve downstream task performance. Typical MPN can be described as follows:
y = ||sign()))))11
(6)
where sG) represents the sign of G. |g| indicates the absolute value of G, a is a scalar, and |1-112 denotes the & normalization operation. MPN can effectively control the preservation of task-specific information during the training process by adjusting the value of a. In particular, as a + 0, the normalized representation ) tends toward becoming in g will be gradually preserved [45]. In the classic MPN method, the value a predefined, and all images share the same value of It can even be deduced from the perspective of Maximum Likelihood Estimation (MLE) that the theoretically optimal value for a is 0.5 [14].
all-ones matrix. As - 1, information
Considering potential significant distribution differences between fine-tuning and inference datasets, in this paper, we lean towards being learnable based on the context adaptively. Specifically, we design the Dynamic Power Normalization (DPN) module in the CFP stage to compute the value of
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(a) DPN, in CFP (b) D in recalibration (sequential)
DPN, in recalibration (parallel)
Figure 3: The DPN module can be placed in both CFP and recalibration stages, which is indicated by DPNc and DPNR, respectively. More importantly, it can be inserted into the Transformer blocks sequentially and parallelly.
based on the value of G as shown in Figure 3(a). Thus, each image can have a different level of task-specific information preservation, enabling more flexible fine-tuning.
With the same consideration, we attempt to use DPN to adaptively preserve task-specific information in recalibration for Parameter Efficiency Fine-Tuning (PEFT), while keeping the backbone network frozen. Given the unique nature the VPR task: the background region in images (e.g.. the building), which may be irrelevant other tasks, serves as crucial clue for place recognition. However, due to the difference in training objectives, a pre-trained VFM may overlook these background regions. Therefore, we employ the DPN enhance the representation of distinctive background regions, and effectively leveraging the representation capabilities of a VFM while minimizing recalibration, as depicted in Figure (b) and (c).
4 Experiments
4.1
Experimental Settings
Choice Visual Foundation Model. To make effective use large-scale unlabeled data, self- supervised transformers are becoming increasingly popular for training VFM. Two self-supervised learning paradigms have demonstrated superior performance for ViT pre-training: contrastive learning- based (e.g., DINO [46]), and masked image modeling- based (e.g.. MAE [47]). There also multi-modal foundation models for robust feature extraction (e.g., CLIP [38]). Recent advanced research [48] demonstrated that VITs pre-trained by the contrastive learning paradigm can produce more universal path-wise representations. In this paper, the ViT model pre-trained by DINOv2, a follow-up of DINO, is adopted as the VFM, which has been verified to have good performance in fine-grained tasks (e.g., depth estimation [37] and VPR [34]). Specifically, three VPR models are fine-tuned based on ViT-S, ViT-B, and ViT-L, named EMVP-S, EMVP-B, and EMVP-L, respectively.
Implementation Details. Both the Fo and Fp branches are implemented by a two-layer MLP network. For fair comparison with SALAD, the output dimensions of Fc and Fp are 128 and 64, respectively. We employ the softmax operation to normalize the output features of Fp. We implement two versions of the DPNR module (i.e., sequential and parallel DPNR). as shown in Figure 3, for the recalibration intermediate features. the parallel version, the original feature is preserved through an independent branch, and updated context is aggregated via element-wise addition. In contrast, the sequential version is equivalent to adding a few extra layers to the backbone. For more implementation details, please refer to Appendix A.1.
Datasets. Many advanced efforts [33; 16; 12] have shown that models trained on the GSV-Cities [33] dataset exhibit strong generalization across various VPR datasets, such MSLS Validation [49], Pittshurgh3kk-tr [50], Pitshurgh250kkees [50], Nordland [51; 52], and SPED [53]. Following the training and validation protocol of these studies, fine-tune the VPR model on the GSV-Cities

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Table 1: Comparison with state-of-the-ar methods. denotes models trained on the GSV-Cities dataset. Due to the high quality of annotations in GSV-Cities, results from models marked with generally outperform those from their corresponding papers. In contrast, results from models without are reported in their respective papers.
(a) Comparison with single-stage methods.
MSLS Val
NordLand= [52]
Pitts250k-tes
SPED
Method
R@
R@5
R@ 10
R@
R@5
R@ 10
R@
R@5
R@ 10
@@
R@5
R@ 10
SPE-VLAD' [54]
78.2
86.8
88.8
25.5
40.1
46.1
89.9
96.1
97.3
73.1
85.5
88.7
Gated NetVLAD* [55]
82.0
88.9
91.4
34.4
50.4
57.7
89.7
95.9
97.1
75.6
87.1
90.8
NetVLAD' [4]
82.6
89.6
92.0
32.6
47.1
53.3
90.5
96.2
97.4
78.7
88.3
91.4
Conv-AP' [33]
83.4
90.5
92.3
38.2
54.8
61.2
92.4
97.4
98.4
80.1
90.3
93.6
CosPlace' [17]
89.9
91.8
34.4
49.9
56.5
91.5
96.9
75.3
85.9
88.6
MixVPR' [16]
88.0
92.7
94.6
58.4
74.6
80.0
94.6
98.3
99.0
85.2
92.1
94.6
EigenPlaces [19]
89.3
93.7
95.0
54.4
68.8
74.1
94.1
98.0
98.7
69.9
82.9
87.6
SALAD' [12]
92.2
96.4
97.0
76.0
89.2
92.0
95.1
98.5
99.1
92.1
96.2
96.5
EMVP-L' (Ours)
97.3
78.4
89.7
96.5
99.1
94.6
97.5
98.4
(b) Comparison with two-stage methods, which include re-ranking stage indicated by F.
MSLS Val
NordLand<* [51]
Pitts30k-tes -estt
Method
R@D
R@5
R@ 10
R@D
R@5
@@ 10
R@D
R@S
R@ 10
3P-SuperGlucf [56]
78.1
81.9
84.3
25.8
35.4
38.2
87.2
94.8
96.4
Patch NetVLAD* [5]
79.5
86.2
87.7
51.6
60.1
62.8
88.7
94.5
95.9
DELG [57]
83.2
90.0
91.1
51.3
66.8
69.8
89.9
95.4
96.7
Trans [10]
86.8
91.2
92.4
58.8
75.0
78.7
89.0
94.9
96.2
R2Former [11]
89.7
95.0
96.2
77.0
89.0
91.9
95.2
SelaVPR4 [13]
90.8
96.4
97.2
85.2
95.5
98.5
92.8
96.8
97.7
TPR w/o re-ranking [10]
70.8
85.1
89.6
15.9
38.6
49.4
73.8
88.1
91.9
SelaVPR (gobal) [13]
87.7
95.8
96.6
72.3
89.4
94.4
90.2
96.1
97.1
EMVP-L (Ours)
97.3
97.6
97.3
99.3
94.0
97.5
dataset, which is pre-trained by the DINOv2 pipeline. Subsequently, Recall (i.e., R@ @@ R@5, and R 10) is evaluated across various VPR datasets as the performance metric.
Model Selection.
We involve 30 epochs for fine- fine-tuning models, and select the one with the highest @ on the Pittsburgh30k-a val dataset for further evaluation on other test datasets. To fairly compare model performance, we repeat the aforementioned process 5 times and calculate the average metrics as the final test results.
4.2
Main Results
We report the performance comparison with SOTA in Table and analyze the results as follows. First, typical VPR methods, re-ranking stage is typically incorporated for retrieved images to enhance the final performance as post-processing. This is primarily due to the inherent noise in the training dataset, which is subject changes in image perspectives, seasonal variations, occlusion, and other factors. We illustrate the comparison through the Trans VPR algorithm with or without the re-ranking stage as an example. Second, supported by the high-quality GSV-Cities dataset, methods (e.g., Mix VPR) without re-ranking achieve comparable performance to those with re-ranking stages. Third, with the further support of DINOv2, the algorithms without re-ranking achieve new levels of accuracy and robustness, as evidenced by the performance of EMVP, Sela VPR (global) and SALAD. Finally, EMVP-L obtains the best performance by leveraging the probing method tailored for the VPR task. Taking the typical MSLS Validation dataset as an example, EMVP-L even outperforms the full fine-tuning method SALAD by 1.7% at Recall @ 1. Additional visualization in Figure shows that the VPR model fine-tuned by EMVP-B successfully finds the closest match in challenging scenarios, including occlusion, illumination change, perspective change, and seasonal variation. For more comparisons with SOTA methods, please refer to Appendix A.I.
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Query
Ground Truth
Top
Top 2
Top 3
REER
Occlusion
Ilumination
Change
Perspective
Change
Seasonal
Variation
Figure 4: Query (gray) and top 3 retrieved frames (green: successful, red: failed). Moreover, of the true (blue) matches is displayed for comparison.
4.3 Ablation Studies
The Impact of Different Probings. We compared probing methods based on first-order and second- order features. Among the first-order methods, the popular LP in classification and GeM pooling in VPR tasks are explored. For second-order feature methods, we dive into the pioneering second-orde method MP in classification and the proposed CFP. Additionally, the buseline can be seen as form of bilinear pooling with the centroids removed, and is implemented by removing the optimal transport operation in the SALAD code base.
As shown in Table the test results LP revealed a significant decrease in performance compared to aggregation based on CFP. One possible reason for this could be the common trick used in LP-based methods, where average pooling is applied to local descriptors obtain fixed-size global one. This first-order statistical features extraction may lead to information loss. GeM pooling generalizes average and max pooling, as a result, its accuracy is still fundamentally limited by first-order features.
Despite that MP has achieved excellent accuracy metrics in classification, its performance in VPR tasks is inferior to CFP, as shown in Table 2. This is mainly due CFP implicitly leveraging the priors provided by semantic centroids. By comparing results of baseline and NetVLAD (ID 2), we find that directly removing centroids using bilinear pooling leads to performance drop. This is why SALAD employs optimal transport to improve the performance. In contrast, this paper introduce CN and DPNc to theoretically refine the simplification of NetVLAD, achieving improved performance.
is noteworthy that advanced methods such as Trans VPR and MiX, employing heavy Trans- former and MLP-Mixer aggregation architectures, have demonstrated excellent performance in VPR tasks. However, these methods do not quite align with the current research paradigm of fine-tuning VFMs based on shallow trainable MLP architectures.
The Impact of the Reinterpretation with Constant Normalization. By comparing the experimental results of ID 2 and ID 10 in Table 2, we can verify that our reinterpretation for NetVLAD has led to improved performance. This is mainly attributed to the climination of the explicit learning of cluster centers, which has reduced parameter number and mitigated the impact of imprecise initialization. is worth noting that increasing the feature dimension of NetVLAD can significantly enhance performance. However, it is essential to consider the potential cost when dealing with the storage of images with sizable global descriptors, particularly in the context of VPR applications.
Comparisons between ID 7 and ID 8, ID 7 and ID 9 in Table 2 demonstrate that the CN makes this reinterpretation operation empirically more robust, and this can be validated by achieving improved performance. Through comparing ID 8 and ID 9 in Table 2, we can observe that the improvement brought by CN dependent on specific implementation. Further exploration of this aspect will be undertaken in our subsequent research, extending beyond the scope of this paper.
8

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Table 2: Comparing different backbones and probings. LP, MP, CFP, CN, and DPNc indicate linear probing, moment probing, centroid-free probing, constant normalization, and dynamic power normalization in probing. respectively. For faimess, results produced by ViT-based models obtained by fully fine-tuning the last 4 blocks. Baseline refers to the simplified NetVLAD adapted by SALAD. The best and the second best results are bolded and underlined, respectively.
ID Mathol
Frx. Dam
Backione
MSLS Val
R3033
R:0
866
NoceLand
R
Rei
Pins lest
R305
R
R01
R30
SPED
10
NEWLAD
32768
KecNet50
K25
39.5
92.0
12.6
07:1
53.3
9815
95.2
57.4
75.7
86.3
2.
NeVVLAD
8192
ViT-B
90.1
95.4
95.6
20.1
85.5
90.2
95.4
98.4
99.1
90.5
95.4
96.7
NetVLDD
24376
95.9
86.5
95.6
98.7
90.5
95.7
LP
765-256
VIT
93.5
38.1
55.3
61.8
91.3
96,9
53.0
92.3
MP
2048
VIT
94.5
95.4
62.6
70,0
92.5
91.3
85.2
926
GeM
8096
VIT
93.9
95.0
35.4
52.5
59.6
89.5
96.5
98.0
83.0
92.1
Baseline
$192+256
VIT-B
90.3
95.7
73.0
78.6
94.6
98.1
99.1
58.0
94.7
95.6
SN--Soflmxx
91.3
95.7
68.0
82.0
86.2
94.9
98.3
99.0
89.1
94.9
96.4
9
CN- Fy
90.5
05.0
96.6
66.4
80.9
64.5
94.5
98.1
99.0
50.0
93.8
95.7
10
DPN, (o.. CFF)
92.6
96.2
95.8
74.6
87.6
91.3
95.2
98.7
99.3
92.1
95.9
07.2
Table 3: Comparing different fine-tuning methods. DPNc and DPNR indicate DPN in CFP and recalibration, respectively. Results of both parallel and sequential versions of DPN are reported. For fairesss, only the last 4 blocks can be fine-tuned, and all methods employ the same backbone, ie., VIT-B. The best and the second best results are bolded and underlined, respectively.
Method
Fine-tuning
Type
Paraen. (M)
MSLS Val
NeniLand RP33
Pins750lk-ted
Ress
AnyLoc
Zoro-shot
6S1.7
78.2
x1.33
16.1
25.4
301.4
87.22
11..4
956.5
35.33
11.4
U5.4
SALAD
Full
27.1
92.2
96.
97.0
76.0
89.2
92.0
95.1
98.5
99.1
92.1
96.2
95.5
CFP
+PSRP
0.14
92.7
96.6
96.9
73.0
86.5
39..5
95.3
98.5
999.2
91.3
55.9
96.9
DPNa(pprs.)
0.05
02.4
06.5
96.8
71.8
85.2
88.9
05.4
98.5
99.1
91.3
96.2
96.7
(6c, EMVP-B)
(PNg(50g)
0.05
93.2
96.9
97.2
76.4
88.8
92.1
95.7
98.9
99.3
91.8
96.5
97.4
The Impact of the Foundation Model. The comparative results between ID 1 and ID 3 in Table indicate that, when the size of the global descriptor is within the same order of magnitude, the VFM (i.e., DINOv2) is more suitable as the backbone network for VPR tasks compared to traditional CNN models. Even in the scenario of zero-shot inference, the VPR model based on DINOv2 demonstrates strong generalization and robustness (refer to AnyLoc in Table 3). However, is important to note that the performance of zero-shot inference still exhibits a substantial performance gap in comparison to methods that are based on fine-tuning. This motivates us to explore a more effective fine-tuning pipeline tailored for VPR tasks. Additionally, the results in Table 10 of Appendix A.2 indicate that as the scale of ViTs increases, the performance of VPR models fine-tuned by EMVP can be improved.
Comparison of Fine-tuning Methods. In Table 3, different fine-tuning approaches are compared. By comparing SALAD and AnyLoc, we can conclude that current VFMs (i.e.. DINOv2) lack sufficient zero-shot reasoning capabilities for diverse data in the VPR domain. SALAD achieves high performance by fully fine-tuning on DINOv2, but VPR models are typically deployed on mobile robots, and this full parameter update approach imposes the higher demands on communication. Therefore, we attempt to study adaptation methods more suitable for VPR tasks.
For fairness, this paper reimplements the advanced PEFT method PSRP [42], which is also aimed cooperating with second-order features, the VPR dataset compare it with our proposed DPNR. Furthermore, as illustrated in Figure 3, the DPNR module can be further divided into parallel and sequential versions for comparative research. By further comparing the sequential and parallel DPNR modules in Table 3, we find that the sequential version performs better. This is primarily because the sequential method recalibrates the backbone features more thoroughly, and does not significantly increase training difficulty since a few additional parameters are introduced. The sequential configuration of the DPNR method is used by default other experiments in this paper. Compared with methods such as SALAD and PSRP, the sequential DPNR outperforms them by achieving the best performance while saving 64.3% of trainable parameters (0. 14M vs 0.05M). We further visualize the impact of DPNr on recalibration in Appendix A.3.
5 Conclusion
In this paper, we have proposed novel fine-tuning pipeline named EMVP, which involves rein- terpreting the classical aggregation (i.e., NerVlAD) into a CFP stage when fine-tuning a VFM for accurate VPR. What is more innovative, both the recalibration and CFP stages employ the same

---

DPN module for task-specific information preservation, effectively conducting PEFT. Extensive experiments conducted on VPR datasets have demonstrated that EMVP can extract more task-specific features, resulting in enhanced accuracy and robustness in VPR performance.
Broader Impacts. This work enhances the safety and efficiency of mobile robots operating in GPS- denied environments. In addition, the potential negative impact is that bad weather and ambiguous scenes will cause certain interference, making difficult to maintain high level of accuracy in VPR.
Limitations. Based on the discussions and comparisons of VFMs such as CLIP, SAM, DINO, and DINOv2 in pioneering works [48; 34; 58]. this paper prioritizes DINOv2 the VFM for experimental analysis. Note that, different VFMs possess distinct capabilities. For example, CLIP can connect images with texts for better interpretabilityy while SAM demonstrates powerful abilities in handling visual prompts. In the future work, we aim to explore these capabilities in different VPR tasks.
Acknowledgements
This research was partially supported by the Zhejiang Provincial Key R&D Program of China under Grant No. 2024C01 166, the Fundamental Research Funds for the Central Universities under Grant No. 226-2024-00227, the National Natural Science Foundation of China under Grant No. 62303428, and the GuangZhou City's 's Key R&D Program of China under Grant No. 2024B01J1301.
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13

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A Technical Appendix
A.1 Implementation Details and More Comparisons
The implementation details are reported in Table 4. To facilitate fair comparison, try to keep the experiment settings as consistent as possible with the comparative methods. We further provide detailed implementation of the Dynamic Power Normalization (DPN), shown by Algorithm 1. The VPR model is fine-tuned on the GSV-Cities dataset, which contains 0.56 million images from 67 thousand different places. Table describes the details of various testing datasets. All experiments are conducted on a NVIDIA RTX A6000 GPU using Py PyTorch. Moreover, fine-tuning a VPR model based on the VIT-B takes 7 minutes per epoch, and requires 21GB GPU memory. Table 6 provides more comprehensive evaluation single- and two-stage methods.
Table 4: Experiment setting for fine- fine-tuning.
Config
Value
Precision
16-mixed
Optimizer
AdamW
Learning rate
1e-3
Weight decay
le-9
Batch size
480
Places
120
Images per place
4
Image size
224 x 224
Number of patches (L)
16 16
Patch size
14 x 14
Output size of Fc (D)
128
Output size of Fp (K)
64
Number of epochs
30
Table 5: Datasets description.
Datasct
Database
Queries
Viewpoint
Scason
MSLS Val
18871
740
Pits250k-test
83952
8280
Nordland
27592
2760
X
SPED
607
607
Algorithm PyTorch Pseudo Code for DPN.
Iclass (ax (odule))
-_init_- (self, D, d, eps):
super (BPEr aelf) self)...Anit...) aelp.avg_pool wn. # D: Dinension of each local descriptor. self. self. pat..projcction = an. S Li.eirr (D,
an Dr.proout ReLU Ln. (d, 1)
10
11
self.activation si.nigg)
13
self sett aps
forvard G):
15
6: Fine grained featurea to be normalized. Number of local descriptors in G.
17
D: Dimension of each local descriptor.
18
L, D G.
avg_ self. (... (-1, squeeee
20
proj - self jet.errrecccciavg.....o
p actf. (proj) unsqueeze (-1)
22
siga torch borch.sign(C) (G)
23
pov_g torch. aba self epa, p. expend (-1,L,)))
return siga pao_c
14

---

Table 6: More comparisons with state-of-the-art methods. The results marked with reproduced based on the official code.
Method
MSL.
Nordl Lande [52)
Pitns250k-test
SPED
Pitt330k-test
Nordi andev [51]
R0
R@5
TPR
70.8 85.1
89.6
10.37
20.97
27.27
70.1
85.2"
89.17
55.77
69.77
76.1
17.88
88.1
91.9
15.9 38.6 49.4
SPE-VLAD' [54]
78.2
86.8
88.8
25.5
40.1
46.1
89.9
96.1
97.3
73.1
85.5
88.7
NeVLLAD' [55]
75.6
NerVLAD"14 [4]
82.6 89.6 92.0
32.6
47.1
53.3
90.5
96.2
97.4
78.7
88.3
91.4
90.07
95.17 96.47
69.47
85.97 91.17
Conv-AP" [33]
83.4 90.5 92.3
38.2
54.8
61.2
92.4
97.4
98.4
80.1
90.3
93.6
90.6
95,
96.2
67.77 85. 90.5"
CocPlace' [17]
56.5
75.3
95.37
70.57
MixVPR' [16]
88.0 92.7 94.6
58.4
74.6
80.0
94.6
98.3
99.0
85.2
94.6
91.67
95.57 96.47
80.47 92.47 95.97
EigenPlaces [19]
89.3 93.7 95.0
54.4
68.8
74,1
94.1
98.0
98.7
69.9
82.9
87.6
92.57
96.77 97.67
65.91
81.97 87.97
SelaVPR (gobal) [13]
87.7 95.8 96.6
43.87
63.17
71.17
92.67
98.07
98.87
83,57
92.6
94.67
90.2
96.1 97.1
72.1 89.4 94,4
SALAD' [12]
92.2
96.4 97.0
76.0
89.2
92.0
95.1
98.5
99.1
92.1
96.2
96.5
92.47
96.31 97.47
89.41
97.27 99.07
EMVP-L/ (Ours)
93,9
97.6
78.4
89.7
92.4
96.5
99.1
94.6
98.4
97.5
98.2
88,7
97.3
99.3
SP-SuperGilue
Parch NetVL.AD* [5]
79.5 86.2 87.7
50.8
34.57
35.47
90.97
96.17 97.27
87.67
93.6"
95.17
88.7
94.5
95.9
51.6
60.1
62.8
DELG [57]
96.7
66.8
69.8
TransVPR [10]
86.8
92.4
46.97
51.67
52.57
88.87
94.27 95.27
85,37
90.87
91.37
94.9
96.2
58.8
75.0
78.7
R2Fomerr [1]]
89.7
95.0 96.2
40.27
46.37 47.37
93.17
97.47 98.47
67.57
75.87
77.8
91.1
95.2
96.3
77.07
89.07
91.97
SclaVPR [13]
81.47
95.7
98.8
89.87
94.77
95.5
98.5
A.2 More Ablation Studies
Dimensions of Fc and Fr. Table and 8 indicate that the output sizes of Fe and Fr have some influence on performance, while the performance is not particularly sensitive to these two hyperparameters. When the output size of Fc (D) ranges from 128 to 512 and the output size of Fp (K) ranges from 64 to 256, the models all achieve good performance.
Numbers of Recalibrated Blocks. Table 9 shows that the number of blocks inserted DPN modules significantly affects the results, and the best results are achieved when the features in the last blocks are recalibrated. However, the selection of recalibrated blocks is beyond the scope of this paper, and future work.
we will further explore
The Scale of ViT Model. Table 10 displays the results of different ViT models at size S, B, and As the scale of VIT models increases, the total number of parameters grows exponentially, making training with full fine-tuning methods extremely challenging. For example, if the ViT-B is replaced with the ViT-L in the full fine-tuning method SALAD, there would a significant decrease in evaluation performance [12]. However, thanks to the introduction of the DPNg module, the number of trainable parameters EMVP only slightly increases with the scale of the ViT, allowing for the full utilization of the enhanced representation capability brought by the larger model size. Due to the limitation in computational resources, ViT-G is not tested.
Table Impact of the output sizes of Fc (K 64).
MSLS Val
Pitts250k test
D
R@D
R@5
R@10
R@1
R@5
R@
32
92.0
96.2
96.8
95.0
98.4
99.0
64
92.4
96.4
96.9
95.5
98.7
99.2
128
93.2
96.9
97.2
95.7
98.9
99.3
256
92.8
96.8
95.5
98.8
99.4
512
92.7
96.2
96.9
95.8
98.8
99.3
Table 8: Impact of the output sizes of Fp (D = 128).
MSLS Val
Pitt2250k-tet
K
R@]
R@5
R@ 10
R@)
R@5
R@ 10
91.5
96.2
96.5
94.6
98.3
99.0
32
91.9
96.2
96.6
95.2
98.8
99.3
64
93.2
96.9
97.2
95.7
98.9
99.3
128
93.0
96.5
97.0
95.8
98.8
99.4
256
92.8
96.6
97.0
95.6
98.8
99.3
15

---

Table 9: Impact of the number of recalibrated blocks.
MSLS Val
Pitts250k
Blocks
R@1
R@5
R@
R@
R@5
R@
90.9
95.7
96.4
94.6
98.2
99.0
2
92.3
96.4
96.9
95.1
98.8
99.2
93.2
96.9
97.2
95.7
98.9
99.3
6
92.2
96.4
96.9
95.2
98.5
99.0
8
89.2
94.6
95.8
94.9
98.3
99.0
Table 10: Comparing different ViT models. Tr. and Ttl. represent the number of trainable and total parameters (M), respectively.
MSLS Val
Pitts250k-test
Arch. Tr/Ttl.
R@1
R@5
R@ 10
R@1
R@5
R@
ViT-S
0.02/21
91.5
95.5
96.8
95.2
99.2 98.9
98.6
VÎT-B
0.05/86
93.2
96.9
97.2
95.7
99.3
VIT-L
0.06/300
93.9
97.3
97.6
96.5
99.1
99.5
A.3
Visualizations for DPNR
The visualization results in Figure 5 indicate that after being trained by DPNR, the top 20% of high-norm tokens tend to appear more in distinctive regions. Figure 6 illustrates that after being fine-tuned through the EMVP pipeline, the VPR model can accurately capture texture details shared among different images. While Figure 7 shows that under changes in perspective, high-norm tokens tend to appear in distinctive background regions, which are typically the tallest building in place. This is primarily attributed the DPNR module adopted by EMVP, which enhances task-specific representations while maximally preserving the feature representation capability of the VFM.
er
(a) Image
(b) Frozen
(c) Full
(d) DPNR
(e) f2 Norm
Figure 5: The visualization of the top 20% high-norm tokens.
Image
Frozen
Full
DPN,
Figure 6: High-norm tokens can contribute to capturing texture details.
16

---

Image
Frozen
Full
DPNR R
Image
Frozen
Full
DPN
Figure 7: Visual place recognition under changes in perspectives.
17

---

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Reviews:
[{"review_id": "y2ncj0LCBv", "reviewer": "Reviewer_M9Bz", "summary": "This work presents a novel pipeline, i.e., EMVP, for visual place recognition based on foundation models. In this pipeline, a Centroid-Free Probing (CFP) method is used to process the output of the foundation model and get the global features of place images. Besides, the authors also propose a Dynamic Power Normalization (DPN) module and incorporate it into both the foundation model and the CFP module, which can improve fine-tuning performance and make the features more task-specific. Extensive experiments demonstrate the effectiveness of the proposed method.", "questions": "see weaknesses", "limitations": "see weaknesses", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The paper is easy to read.\n2. The proposed method is novel.\n3. The experimental results are good and outperform SOTA methods.", "weaknesses": "1.\tThe biggest problem with this paper is that there are many inaccuracies. For example:\n\na.\tThis paper claims that NetVLAD is a second-order feature, but it is actually a first-order feature. Please refer to the description in the NetVLAD paper [1] (“the NetVLAD layer aggregates the first order statistics of residuals”) and this literature [2] (\"the original NetVLAD method utilizes only the first-order statistical information\").\n\nb.\tThe authors claim that “In this paper, we resort to post normalization methods (e.g.,\nsoftmax and L2 normalization) to constrain the value of…to be constant”. In fact, NetVLAD also has softmax and L2 operations, which cannot yield that the summation term in equation (4) is a constant. Given a local feature, I know that the sum of the probabilities of assigning it to all clusters is a constant due to the softmax. But given a cluster, the sum of the probabilities of all local features assigned to it isn’t a constant. I think the author's deduction is wrong. That is, the global feature G in equation (5) is different from equation (3).\n\nc.\tThere are many missing results in Table 1. The results of other methods are directly copied from other papers. In fact, the existing papers used different versions of the Nordland dataset, and the authors do not realize this and compare the results of the different versions of Nordland together, which is wrong.\n\n2.\tThe Dynamic Power Normalization (DPN) module has a similar structure to the classic adapter, and its functions are basically the same. However, the author does not explain the difference between the two, or what improvements DPN brings to the adapter. The parameters of the Linear Layer in DPN are also not provided.\n\n3.\tIn the ablation experiment, the authors call the baseline “the simplified version of NetVLAD adapted by SALAD”, which is not appropriate. This baseline does not use softmax and L2, which is essentially different from NetVLAD.\n\n4.\tIn Table 1 (the comparison to other methods), the SALAD method is based on DINOv2-Base. The authors provide the results of the proposed method only based on DINOv-Large. It is more appropriate to provide those of both DINOv-Large and DINOv2-Base.\n\n[1] Arandjelovic, Relja, et al. \"NetVLAD: CNN architecture for weakly supervised place recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.\n\n[2] Chen, Boheng, et al. \"A novel localized and second order feature coding network for image recognition.\" Pattern Recognition 76 (2018): 339-348.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for carefully reading our paper and recognizing its writing and novelty. We appreciate the opportunity to address your questions:\n\t\n**1.a.** We posit that the second-order statistics (bilinear features) employed in NetVLAD are implemented through the **outer product** of two vectors corresponding to each location of an image. This interpretation is rooted in the seminal work [1], which pointed out that **\"VLAD can be written as a bilinear model\"**. Consequently, our paper adheres to the definition of second-order statistics as established in the research line of [1], [2], and [8]. Otherwise, the mentioned LSO-VLADNet [3] applied the **\"element-wise square operation\"** to first-order features (residuals), and considered it as a second-order statistic operation. \n\nWe also agree that both [3] and [4] consider residuals as first-order features, a definition that does not take into account the soft-assignment. However, if the soft-assignment is seen as another type of first-order feature extracted by MLP Layers, then the features ultimately output by NetVLAD are second-order. Specifically, Eq. (4) in [4] can be understood as computing the **outer product** of each local residual $(x_i - c_k)$ with the soft-assignment vector $\\bar{a}(x_i) = [\\bar{a}_1(x_i), \\bar{a}_2(x_i),\\ldots, \\bar{a}_k(x_i)]$, and then using sum pooling to obtain global second-order features. Note that the process is also formulated by Eq. (5) in our paper.\n\n**1.b.** Softmax in NetVLAD is introduced to ensure that the sum of the probabilities of assigning a local feature to all clusters is a constant, which can be formulated as $soft\\\\_assign = F.softmax(soft\\\\_assign, \\mathbf{dim=1}), soft\\\\_assign \\in \\mathbb{R}^{B \\times C \\times L}$. $B$, $C$, and $L$ denote batch size, the number of clusters, and the number of local features, respectively. Moreover, NetVLAD adapts intra- and L2-normalization to reduce the effect of bursty image features, as described in paper [5] \"intra-normalization fully suppresses burst\". The intra-normalization  conducted on the features weighted by soft\\_assign can be formulated as $vlad = F.normalize(vlad, p=2, dim=2), vlad \\in \\mathbb{R}^{B \\times C \\times D}$ where $D$ denotes the dimension of local feature.\n\nThe motivation of our post normalization method is to remove the cluster centers, as described in [5] ``VLAD similarity measure is strongly affected by the cluster center''. Accordingly, the post constant normalization is introduced to ensure the sum of the probabilities of all local features assigned to a cluster is constant, which can be formulated as $soft\\\\_assign = F.softmax(soft\\\\_assign, \\mathbf{dim=2}), soft\\\\_assign \\in \\mathbb{R}^{B \\times C \\times L}$. Note that our EMVP **retains** both intra- and L2-normalization in NetVLAD.\n\n**1.c.** Thank you very much for pointing out this error. The Nordland dataset exists in two versions, and the version used in our paper is provided by [6]. As shown in the table below, we display the results for the other version provided by [7]. EMVP even surpasses the previous best method (SelaVPR) with a re-ranking stage.  In the revised version, we will separately and clearly present the test results for different versions of Nordland dataset.\n\n|Method||Nordland-test||\n|-|-|:-:|-|\n||R@1|R@5|R@10|\n|SelaVPR(global)|72.3|89.4|94.4|\n|SelaVPR(re-rank)|85.2|95.5|98.5|\n|**EMVP-L(ours)**|88.7|97.3|99.3|\n\n**2.** Our DPN module leverages power-law operations to effectively alter the magnitude relationships between local features, surpassing the capabilities of multiplication operations employed in classic adapters. This empowers the DPN module to accentuate discriminative features, enhancing its ability to capture task-specific information as discussed in lines 181 to 187. Note that controlling the preservation of task-specific information through changing the power value is also supported by previous theoretical research [8]. The superiority of the proposed DPN is evidenced by the empirical results in Table 3. Compared to the advanced adapter method PRSP, EMVP achieves notable improvements in the Recall@1 by 0.5\\%, 3.4\\%, 0.4\\%, and 0.5\\% on MSLS Val, NordLand, Pitts250k-test, and SPED, respectively. Remarkably, these enhancements are accompanied by a substantial 64.3\\% reduction in parameters, highlighting the efficiency and scalability of the DPN module.\n\nIn addition, the implementation of DPN is shown as Algorithm 1 in Appendix A.1. Specifically, the input and output dimensions of the linear layer in $\\mathrm{DPN_C}$  are 128 and 64, respectively, and the former corresponds to the output size of $\\mathcal{F}_C$. While, the input and output dimensions of the linear layer in $\\mathrm{DPN_R}$  are $d$ and $d/2$, respectively, where $d$ is the dimension of the ViT model. In the case of the ViT-B model, $d=768$.\n\t\n**3.** SALAD also uses L2-normalization, as described in paper [9] \"Following NetVLAD, we do an **L2 intra-normalization** and an entire L2 normalization of this vector.\". SALAD and the baseline do not employ softmax, and they differ from NetVLAD to some extent. Therefore, we will revise the description of the baseline in the updated version to: \"the baseline can be seen as a form of bilinear pooling with the centroids removed, and it is implemented by removing the optimal transport operation in the SALAD code base\".\n\t\n**4.** As shown in the table below, our EMVP achieves the best results under different backbone configurations. For instance, compared to the existing single-stage method based on DINOv2-L, EMVP-L improves Recall@1 on MSLS Val by 6.2\\%.\n|Method|Backbone||MSLSVal|||Pitts250k-test|||SPED||\n|-|-|-:|:-:|:-|-:|:-:|:-|-:|:-:|:-|\n|||R@1|R@5|R@10|R@1|R@5|R@10|R@1|R@5|R@10|\n|SALAD|DINOv2-B|92.2|96.4|97.0|95.1|98.5|99.1|92.1|96.2|96.5|\n|**EMVP-B(ours)**|DINOv2-B|93.2|96.9|97.2|95.7|98.9|99.3|91.8|96.5|97.4|\n|SelaVPR(global)|DINOv2-L|87.7|95.8|96.6|-|-|-|-|-|-|\n|**EMVP-L(ours)**|DINOv2-L|93.9|97.3|97.6|96.5|99.1|99.5|94.6|97.5|98.4|", "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": "f1pVaMk2w2", "reviewer": "Reviewer_cxjU", "summary": "The paper presents a method for parameter-efficient fine-tuning of Visual Foundation Models (VFMs) for the Visual Place Recognition (VPR) task. The method includes a DPN module, which is placed between frozen VFM blocks to recalibrate features for the VPR task, and an aggregation (probing) module named Centroid-Free Probing (CFP), a simplified version of NetVLAD.\nThe DPN module enables the VFM to focus more on features that are important for the VPR task, such as background objects. The CFP module addresses the costly centroid initialization problem of NetVLAD and enhances generalization.\nThe authors conduct experiments demonstrating the effectiveness of the proposed method compared to existing state-of-the-art (SOTA) VPR methods.", "questions": "- Can you provide more detailed information on the training procedure, including the loss function, sampling strategy for batches, and any data augmentation techniques used?\n- The paper mentions limitations and future work but could benefit from more concrete plans or proposals to address these limitations. Can you elaborate on your future work plans to address the identified limitations, such as handling adverse weather conditions and ambiguous scenes? What specific approaches are you considering?", "limitations": "The paper adequately addresses that the lack of analysis of different VFM backbones is a major limitation.", "rating": 5, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- The paper is devoted to the use of Visual Foundation Models (VFMs) in the Place Recognition task, a relevant and promising area of research.\n- The structure of the paper is well-organized and easy to follow, with the main contributions explicitly highlighted in the introduction. Each subsection of the related work clearly defines the paper's place in the existing research. The method section introduces the preliminaries and then explains the proposed solution in detail.\n- The proposed method outperforms existing state-of-the-art (SOTA) methods.", "weaknesses": "- Minor weakness: The large number of abbreviations (CFP, CN, DPN_C, DPN_R, EMVP, PEFT, etc.) makes the paper a bit hard to read.\n- The training details are incomplete, missing the loss function and the training procedure itself (e.g., how the batches were sampled).\n- The used metrics are not explained at all. While these are standard metrics for Place Recognition, the paper would benefit from a brief explanation.\n- Although the proposed method outperforms existing state-of-the-art (SOTA) methods, the overall novelty of the paper is moderate. It represents more of an iterative improvement of existing results rather than a fundamentally new method.\n- When using large Visual Foundation Models, a major drawback is their computational costs, which may affect the applicability of the proposed method. Information about the models' time and memory consumption is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your insightful comments, we provide the feedback as follows:\n\n**Q1&W2**. Detailed information on the training procedure. To ensure a fair comparison, we have kept the details unrelated to the innovations of this paper consistent with previous SOTA methods (i.e., Conv-AP [12], MixVPR [15], and SALAD [9]) during the training process. Therefore, the loss function, sampling strategy for batches, and data augmentation techniques remain the same with the above SOTA methods. Specifically, the model is trained by Multi-Similarity loss [10]. We use batches containing 120 places, each represented by 4 randomly sampled images, resulting in batches of 480 images, as shown by Table 4 in Appendix A.1. Training images are resized to $224 \\times 224$, and no data augmentation technique is applied. \n\n**Q2**. Limitation and future work. Current VPR works (e.g., EMVP, SALAD, and SelaVPR) rely solely on extracting more generalized visual features based on VFMs. We have observed a recent trend in large multi-modal models towards lightweight and unified designs, represented by Florence-2 [11], where the size of backbone is only 0.23B, and different image-text understanding tasks are being unified into the Visual-Question-Answering task. We are currently investigating how to guide the model's reasoning ability in adverse weather and ambiguous scenes by constructing multi-modal thought-of-chains.\n\n**W1**. Thanks for patiently reading our paper. We will reduce the number of abbreviations in the revised version.\n\t\n**W3**. Evaluation metrics. We use the standard  Recall@k evaluation metric, following [4; 9; 12; 13; 14; 15]. A query image is considered successfully retrieved if at least one of the top-k retrieved reference images is within 25 meters of the query.\n\n**W4**. Novelty of the paper. We are among the first to explore probing techniques for the VPR task. Specifically, our CFP admits a theoretical and empirical justification for the simplification of NetVLAD, fixing interpretability and performance issues that were present otherwise. Note that, our CFP improved Recall@1 on NordLand by 2.8\\% compared to NetVLAD, while also reducing the memory for descriptors by 66\\%. Reviewer M9Bz and psQJ have also recognized our work's novelty, highlighting the value of our contributions.\n\t\n**W5**. Time and memory consumption. We compare both single-stage and two-stage methods in the table below. Without using re-ranking, SALAD and our EMVP outperform all other methods while being orders of magnitude faster. Results marked with $^\\diamondsuit$ are computed using a RTX 4090 GPU. Memory footprint is calculated on the MSLS Val dataset, which includes around 18, 000 images. The evaluation protocol and code are provided by R2Former [16]. Note that, the table below reports test results of Pytorch models. If TensorRT is used for acceleration in the deployment, the applicability of the proposed method on mobile devices (such as unmanned vehicles) will be greatly improved.. The introduction of VFMs increases the backbone size, but it simultaneously reduces the dependence on re-ranking, saving more latency time and memory.\n| Method | Global feature size | Local feature size | Memory (GB) | Retrieval (ms) | Reranking (ms) |\n|----------|:----------------------:|:--------------------:|:---------------:|:----------------:|:-----------------:|\n| Patch-NetVLAD | 4096 | 2826$\\times$4096 | 908.30 | 9.55 | 8377.17 |\n| TransVPR | 256 |1200$\\times$256 | 22.72 | 6.27 | 1757.70 |\n| R2Former | 256 | 500$\\times$131 | 4.7 | 8.88 | 202.37 |\n| SelaVPR$^\\diamondsuit$ | 1024 |$3721\\times128$ | 32.01 | 6.87 | 150.78 |\n| SALAD | $8192 + 256$ | 0.0 |0.63| 2.41 | 0.0 |\n| SALAD$^\\diamondsuit$ | $8192 + 256$ | 0.0 | 0.57 | 1.41 |0.0 |\n| **EMVP-B$^\\diamondsuit$ (ours)** | $8192 + 256$ | 0.0 | 0.57 | 1.42 |0.0 |\n| **EMVP-L$^\\diamondsuit$ (ours)** | $8192 + 256$ | 0.0 | 0.57 | 3.66 |0.0 |", "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": "289lVGLdTC", "reviewer": "Reviewer_psQJ", "summary": "Existing VPR models often need to be trained from scratch on environment-specific data, resulting in insufficient generalization performance. The paper aims to improve environment generalization by fine-tuning a visual foundation model. Specifically, it uses DINOv2 as the foundation model and enhances its feature extraction capabilities for VPR tasks by introducing novel probing method and adaptor.", "questions": "Please refer to the Weaknesses.The technologies used in the paper do not exhibit any evident negative social impacts.", "limitations": "The technologies used in the paper do not exhibit any evident negative social impacts.", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The proposed method is novel. It designed the DPN module to automatically learn the value of power based on the context.\n2. The motivation of the paper is clear, and the experimental results are both visualized and quantified adequately. The implementation details are described clearly.\n3. The proposed method has highly parameter efficiency compared to the current state-of-the-art method.", "weaknesses": "1. The explanation for why NetVLAD is the most popular aggregation method is lacking in lines 90-91. This directly impacts the reason of choosing NetVLAD as the starting point for probing in the paper.\n\n2. Why do models trained on the GSV-Cities dataset achieve high accuracy on other datasets as shown in Table 1? Further explanation is welcomed.\n\n3. The article makes some designs for deploying VPR models on mobile devices, such as introducing linear layers for dimensionality reduction and proposing DPN for PEFT. However, I am concerned whether ViT series models, which have large parameter sizes, are suitable for deployment on mobile devices.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for acknowledging our motivations and experimental results.\n\t\t\n1. The VPR task involves a more fine-grained classification, as two images of the same location may have minimal overlap and require identification based on features from a small region. As discussed in the introduction of this paper, NetVLAD can be seen as a second-order feature, which provides an accuracy advantage over first-order features used by alternative techniques. This perspective is also supported by many experimental results in the field [12; 15; 17; 18; 9].\n\t\t\n2. Mainstream VPR datasets (e.g., Pittsburgh250k) use GPS coordinates to determine positive and negative examples. In fact, images with distant GPS coordinates may contain common buildings, while images with nearby coordinates may have no common objects due to different orientations. In other words, the labels in current mainstream VPR datasets are noisy. Consequently, some cutting-edge works have improved the data efficiency by refining the dataset construction methods [12; 19; 20].\n\t\t\n3. On one hand, with the advancement of computing power, ViT-based models are increasingly employed in various cutting-edge research areas of mobile robotics, such as localization [9; 21], perception [22], and manipulation [23]. On the other hand, the size of foundation models is continuously decreasing. For example, the 0.23B Florence-2 [11] can rival the performance of the 80B Flamingo. Therefore, it is feasible for us to study VPR tasks based on foundation models.", "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": "prahiA0s0b", "reviewer": "Reviewer_UsaF", "summary": "This paper aims to fine-tune a visual foundation model for visual place recognition, with an innovative focus on the probing stage. Specifically, it makes three contributions: \n\n1. It proposes a probing method called CFP and introduces a normalization method named DPN within the CFP.\n\n2. It provides theoretical and experimental support for the proposed probing and normalization methods.\n\n3. It integrates DPN as an adapter into the backbone stage to enhance the performance.", "questions": "1. What is the relationship between the VPR and the image classification task? Why are the LP and MP methods in the image classification task not suitable for direct application to the VPR task?\n\n2. In Table 8, why did increasing the number of recalibrated blocks not lead to a performance improvement?\n\n3. As discussed in line 155, NetVLAD can be implemented with bilinear pooling. How does it perform compared to CFP?", "limitations": "The paper thoroughly discusses the limitations and potential social impacts. Currently, the application of VPR is limited by its insufficient accuracy. However, research in this area undoubtedly contributes to the improvement of mobile robot safety.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The paper is well-organized and easy to follow.\n\n2. The paper proposes a Centroid-Free Probing (CFP) stage for fine-tuning a visual foundation model in VPR tasks, providing a novel and task-specific probing method. CFP eliminates the need for explicit calculation of semantic centroids in typical NetVLAD, by introducing a simple and effective Constant Normalization (CN) operation.\n\n3. The paper thoroughly discusses related probing techniques (LP, MP, GeM, NetVLAD), which are supported by extensive experiments. Moreover, EMVP achieves SOTA performance in mainstream VPR datasets.\n\n4. Interestingly, the DPN module can be used for post-processing during the probing stage and as an adapter for parameter efficiency fine-tuning.", "weaknesses": "Some details need to be more clearly expressed, and some experimental phenomena need more detailed explanations. Please refer to the ''Questions''.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for acknowledging our paper, and we appreciate your valuable suggestions.\n\n1. As discussed in our response to reviewer psQJ, LP is a kind of first-order feature and less accurate compared to second-order features in the fine-grained VPR task. MP is proposed solely for coarse-grained classification tasks, with a focus on accelerating the computation of second-order features, which limits the accuracy.\n\t\t\n2. This phenomenon was also observed by the authors of SALAD. More thorough fine-tuning can conceptually yield better domain performance, but it needs more data. Therefore, there is a correlation between the amount of data and the optimal number of recalibrated blocks.\n\t\t\n3. The *baseline* in Table 2 is the bilinear form of NetVLAD (with the centroids removed). We implemented it by removing the optimal transport operation in the SALAD code base. After introducing the constant normalization and $\\mathrm{DPN_C}$ layer, our CFP achieves a significant improvement over the baseline. For example, it improves Recall@1 on MSLS Val by 2.3\\%.", "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:
Dear Reviewers,

Thanks for your hard work. Your constructive comments will help us continuously improve this paper. We list all the references mentioned in our rebuttals for your convenience.

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[11] Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan. Florence-2: Advancing a unified representation for a variety of vision tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4818–4829, 2024.

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[15] Amar Ali-Bey, Brahim Chaib-Draa, and Philippe Giguere. Mixvpr: Feature mixing for visual place recognition. In IEEE Winter Conf. Appl. Comput. Vis., pages 2998–3007, 2023.

[16] Sijie Zhu, Linjie Yang, Chen Chen, Mubarak Shah, Xiaohui Shen, and Heng Wang. R2former: Unified retrieval and reranking transformer for place recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19370–19380, 2023.

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[18] Feng Lu, Lijun Zhang, Xiangyuan Lan, Shuting Dong, Yaowei Wang, and Chun Yuan. Towards seamless adaptation of pre-trained models for visual place recognition. In Int. Conf. Learn. Represent.

[19] Gabriele Berton, Gabriele Trivigno, Barbara Caputo, and Carlo Masone. Eigenplaces: Training viewpoint robust models for visual place recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11080–11090, 2023.

[20] María Leyva-Vallina, Nicola Strisciuglio, and Nicolai Petkov. Data-efficient large scale place recognition with graded similarity supervision. In IEEE Conf. Comput. Vis. Pattern Recog., pages 23487–23496, 2023.

[21] Hanwen Jiang, Arjun Karpur, Bingyi Cao, Qixing Huang, and André Araujo. Omniglue: Generalizable feature matching with foundation model guidance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19865–19875, 2024.

[22] Haoyu Zhen, Xiaowen Qiu, Peihao Chen, Jincheng Yang, Xin Yan, Yilun Du, Yining Hong, and Chuang Gan. 3d-vla: A 3d vision-language-action generative world model. In Forty-first International Conference on Machine Learning.

[23] Brianna Zitkovich, Tianhe Yu, and Sichun Xu et al. RT-2: Vision-language-action models transfer web knowledge to robotic control. In 7th Annual Conference on Robot Learning, 2023.

Meta-review: All reviewers ultimately provided positive recommendations for the paper. The AC has determined that the novelty and effectiveness of this work meet the standards for acceptance. Additionally, the reviewers' comments contain several suggestions that could further improve the paper, , as reflected in the authors' responses. The AC encourages the authors to use them to update the manuscript.
Decision: Accept (poster)