Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era
Authors: Feng Lu
Tong Jin
Canming Ye
Xiangyuan Lan
Yunpeng Liu
Chun Yuan
Venue: NeurIPS 2025 poster ()
Keywords: Visual Place Recognition
Image Descriptors
Aggregation
Primary area: applications

Abstract: Visual place recognition (VPR) is typically regarded as a specific image retrieval task, whose core lies in representing images as global descriptors. Over the past decade, dominant VPR methods (e.g., NetVLAD) have followed a paradigm that first extracts the patch features/tokens of the input image using a backbone, and then aggregates these patch features into a global descriptor via an aggregator. This backbone-plus-aggregator paradigm has achieved overwhelming dominance in the CNN era and remains widely used in transformer-based models. In this paper, however, we argue that a dedicated aggregator is not necessary in the transformer era, that is, we can obtain robust global descriptors only with the backbone. Specifically, we introduce some learnable aggregation tokens, which are prepended to the patch tokens before a particular transformer block. All these tokens will be jointly processed and interact globally via the intrinsic self-attention mechanism, implicitly aggregating useful information within the patch tokens to the aggregation tokens. Finally, we only take these aggregation tokens from the last output tokens and concatenate them as the global representation. Although implicit aggregation can provide robust global descriptors in an extremely simple manner, where and how to insert additional tokens, as well as the initialization of tokens, remains an open issue worthy of further exploration. To this end, we also propose the optimal token insertion strategy and token initialization method derived from empirical studies. Experimental results show that our method outperforms state-of-the-art methods on several VPR datasets with higher efficiency and ranks 1st on the MSLS challenge leaderboard. The code is available at https://github.com/lu-feng/image.

Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era
Feng Lu
1,2x
Tong Jin Canming Ye Yumpeng Liu Xiangyuan Lan Chun Yuan
Tsinghua Shenzhen International Graduate School, Tsinghua University
Penggcheng Laboratory
*Shenyang Institute of Automation, Chinese Academy of Sciences 4 University of Chinese Academy of Sciences 'Pazhou Laboratory (Huangpu)
luf anfenrv@@gail.. com
ycm248mails tsinghua. edu, cn ypliu@sia.cn lanxy@ pel.ac.cn yuanc @ www.sigguaaaccuu..oo
jintongesia.co
Abstract
Visual place recognition (VPR) is typically regarded specific image retrieval task, whose core lies representing images as global descriptors. Over the past decade, dominant VPR methods (e.g., NetVLAD) have followed paradigm that first extracts the patch features/tokens of the input image using a backbone, and then aggregates these patch features into global descriptor via an aggregator. This backbone- wicckoon--luu-aggreeee plus-aggregator paradigm has achieved overwhelming dominance the CNN era and remains widely used in transformer-bas bassed models. this paper, however, we argue that a dedicated aggregator is not necessary in the iransformer era, that is, we can obtain robust global descriptors only with the backbone. Specifically, are prepended the patch tokens before particular transformer block. All these tokens will be jointly processed and interact globally via the intrinsic elf-attention mechanism, implicitly aggregating useful information within the patch tokens to the aggregation tokens. Finally, we only take these aggregation tokens from the last output tokens and concatenate them the global representation. Although implicit aggregation can provide robust global descriptors an extremely simple manner, where and how to insert additional tokens, as well as the initialization tokens, remains an open issue worthy of further exploration. To this end, we also propose the optimal token insertion strategy and token initialization method derived from empirical studies. Experimental results show that our method outperforms state-of-ther oftthe-art methods on several VPR datasets with higher efficiency and ranks 1st on the MSLS challenge leaderboard. The code is available at
introduce some learnable aggregation tokens, which
com/ cem/lu-feng// image
Introduction
Visual place recognition (VPR) involves identifying the coarse geographical location of query place image by retrieving the most similar images from geo-tagged database captured at previously visited places [46]. It fundamental and essential task in wide range of computer vision and robotics applications, e.g., augmented reality [53), autonomous driving (21). and SLAM (15). Thus, it has garnered significant attention and study. Despite recent advances, there still exist some challenges in VPR, including condition variations, viewpoint changes, and perceptual aliasing (images from different places showing high similarity) 46], etc.
"Equal contribution.
i Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Typically, VPR is formulated as an image retrieval problem. For given query image and database. all place images are represented using global features, and the nearest neighbor search is conducted in this feature space to get the target place images that best match the query. The global features are usually obtained by employing aggregation methods (e.g., VLAD 35)) to process local features. With the advancement of deep learning. most VPR methods have used a convolutional neural network (CNN) [31] or vision transformer (ViT) [23] as the backbone to extract local (patch) features. Meanwhile, NetVLAD 4 and GeM pooling [56] have become the most popular aggregation methods for aggregating local features to yield global descriptors, which are generally robust against common visual variations. Following this paradigm, some recent studies proposed more aggregation methods (e.g., MiXVPRRI (2). SALAD [34,, Crica VPR 44), BoQ 31, and EDTformer (37), trying to make the global features condition- and viewpoint- neewponn-invariinn, thereby achieving a promising performance.
VPR paradigm to obtain global features has become the de-facto standard [10] in the CNN era, has some potential issues. Finst, the two-stage process (feature extraction + aggregation) may lead to unnecessary structural complexity and redundancy. Second, the one-shot aggregation of patch features implemented by the aggregator offers no opportunity for correc- tion and refinement. Regarding specific aggregation methods (uggregators), there may exist some particu- lar issues, such as the loss of position information of original patch features in NetVLAD 4 Designing a perfect aggregator artificially is highly challenging. However, in light of the nature of transformer-based backbones, which are capable of modeling global contextual information and long-range dependencies [24], we argue that it is no longer necessary to de- sign an aggregator separately. Instead, we can lever- age the intrinsic celf-attention mechanism within the backbone to implicitly aggregate useful information from patch tokens, thereby eliminating the need for an extra aggregator. In fact, previous work BoQ 3 also attempted to utilize self- and cross-attention mechanisms to aggregate useful information, yet still introduced an extra aggregator that includes en- coder blocks and cross-attention layers, as well as
Although this beekkhone-plu-aggreen
Recalla on Pta33
Panmities Aggresato
94.5
10
8.626
91
94
(M
Recalli1
93.5
93.1
6
93
$2.8
4
52.5
925
.411
92
0011
NHVLAD SALAD
BoQ
ImAggesouzz
Desciptos Dimariaoriiity
Irfennze Time
14000
12228
Dimerstorsitty
12000
164
16.5
immge
163
16
10000
8448-
15.5
15
15
6000
6144
14.8
4000
14
NeVLAD SALAD BoQ
ImAgeionnt
Figure 1: Comparison of three explicit aggre- gation methods and our ImAge. All methods use MINOOv-base-rege are trained on the VSV-Cities dataset. ImAge achieves the best Recall @ with the smallest descriptor dimension and the lowest inference time. Meanwhile, there is no extra explicit ag- gregator in our ImAge model.
as the backbone and
large number of learnable queries. Another study [18] indicated that simply adding some registers, similar to concatenating the class token with patch tokens, can buffer excess global information (so-called "undesirable artifacts") into these registers. Unfortunately, it discarded these registers finally and lacked deeper research on the use of global information on registers.
In view of the issues of the previous explicit aggregation paradigm and the potential implicit aggrega- tion ability of the transformer backbone itself, in this paper, we systematically explore the Implicit A Aggregation (abbreviated as ImAge) method, i.e., unify feature extraction and aggregation solely via the backbone for VPR. Specifically, we introduce some learnable aggregation tokens, which are prepended to the patch tokens before particular transformer encoder block. All these tokens will be jointly processed by the subsequent blocks and interact via the intrinsic self-attention mechanism, thus transmitting useful information within the patch tokens to aggregation tokens. Finally, we only take aggregation tokens from the output of the last block and concatenate them to serve as the global descriptor, thereby achieving implicit aggregation. The proposed VPR paradigm provides novel perspective different from the previous paradigm, unifying feature extraction and aggregation into more cohesive framework. This further enables progressive aggregation in cascaded transformer blocks (rather than one-shot aggregation by a separate aggregator), thus achieving the correction and refinement of global image representations (i.e., our aggregation tokens). Moreover, where and how to add aggregation tokens, as well as the initialization of these tokens, significantly impact perfor- mance. To this end, we propose an optimal token insertion strategy and token initialization method to effectively and efficiently yield more robust image representations and thus achieve excellent VPR performance. Our ImAge brings the following contributions:
2

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1) We propose an implicit aggregation method to produce robust VPR image representations, which neither modifies the backbone nor needs an extra aggregator. It only adds some aggregation tokens before a specific block of the transformer backhone, leveraging the inherent self-attention mechanism to implicitly aggregate patch features. Our method provides a novel perspective different from the previous paradigm, effectively and efficiently achieving better performance in the transformer era.
2) To further improve the performance and efficiency of our ImAge, we propose: a) an aggregation token insertion strategy that deliberately delays token insertion until a specific transformer block, where patch tokens have acquired sufficient representation capabílity; b) a token initialization method that uses the L2- nonormalized cluster centers yielded by the k-means method to initialize added tokens.
3) Extensive experiments show that our ImAge significantly outperforms the latest explicit aggregation methods ge.g. SALAD and BoQ) with the same setup (see Fig. [ Besides, our method also achieves state-of-the-art (SOTA) results (e.g., ranks 1st on MSLS challenge leaderboard) with high efficiency.
2 Related Work
Visual Place Recognition:
Early research on VPR primarily focused on aggregating the hand-crafted descriptors 7 to global descriptors using some classical aggregation algorithms, such as Bags
of Words 660 and VLAD 35|
In light of the remarkable achievements of deep learning across numerous computer vision tasks, contemporary VPR approaches [6]
45
38 17 54
26 (2 [0] 28 [1 (2 have increasingly utilized diverse deep features for better performance.
Besides, traditional aggregation algorithms are gradually replaced by trainable aggregation layers,
e.g., NetVLAD 4 and GeM pooling [56]. Although some methods 30 9 59
47 50 employ
local feature matching for re-ranking after initial global feature retrieval to boost performance, the caaonne-puu-agggreee
paradigm has been the de-facto standard [10] in VPR over the past decade.
Some recent research S [ 34 49 has proposed several alternative approaches following this
paradigm. For instance, CricaVPR [49 leveraged a cross-image encoder to produce cross-image correlation-aware global representations. SALAD
K redefined the soft assignment NetVLAD as
an optimal transport problem and used the Sinkhorn algorithm to solve it. BoQ (3) employed distinct facilitating better information
learnable queries to probe the input features through cross-attention aggregation. These methods achieved excellent results using the ViT-based foundation model DINOv2 as the backbone. Unlike these methods that meticulously design auxiliary aggregators to yield global features, our ImAge method presents novel paradigm that only introduces some additional tokens to the transformer backbone to conduct implicit aggregation via the inherent self eeff-attention mechanism in transformers, thus achieving simpler architecture and more powerful performance.
Additional Tokens in Transformers: Popularized by BERT 20), integrating special tokens into the token sequence in transformers has been a promising design choice for various purposes. We group such extra tokens into 3 categories based on their functional roles. 1) Output-oriented tokens are learnable anchors that collect information from patch tokens, whose output values then transmitted as task- task-specific outputs, e.g., the class tokens used in BERT (20) and ViT (23) for classification, as well as detection tokens in YOLOS [25] for object detection. 2) Prompt tokens act trainable continuous vectors that replace traditional discrete text prompts, efficiently guiding pretrained transformer models to adapt to specific tasks by adjusting the model input, without modifying the parameters of models [43 42 36), which has become an essential branch of parameter-efficient fine- fine-tuning methods [32] 3) Memory lokens act as registers that hold intermediate states during sequential processing steps, tracing their roots to neural memory architectures 14, 13 This approach gains critical support from the DINOv2-register work [18. which observed that vision transformers improperly re-purpose background patch tokens as implicit memories when the standard class token lacks the capacity to accommodate global semantics. To address this, they prepend multiple memory tokens called registers to input tokens, which provide extra storage for buffering of global context, thus eliminating artifacts. Inspired by this work, we introduce the concept of aggregation tokens to effectively absorb global context from patch tokens. However, register tokens are discarded from the final output after temporary use, contrasting with our aggregation tokens that directly form the output descriptor for VPR (i.e., our method falls into the "output- orienttt-oeeeeddddee tokens" category). Among VPR methods, BoQ (3 also advocated the introduction of bag of output-oriented tokens named queries for aggregation (but in the aggregator rather than backbone). While effective, BoQ uses extra encoder blocks and cross-attention layers as the aggregator. In contrast, our method directly employs the inherent self-attention mechanism of the backbone, offering unique ad advantages.
3

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Queries
Backboone
(NIT)
Aggregater
Image
Backbone (VIT)
Appregator
presious paradigm traditional aggregation.
(b) previous paradigm queries-based aggregation.
Our implicit aggregation pipelline
Agu Tokens Initialization
Agt Tokens
Platten &
Posttton
Norma Global
Patch Embedding
Embeddiing
Enzoder Blocks
Encoder
Featrre
Diacadd
Imagy
femeans choders
(c) Our implicit aggregation paradigm (without explicit aggregator).
Figure 2: Illustration of the previous paradigm and our ImAge paradigm. (a) The backbone-plus aggregator paradigm with the traditional aggregator. (b) The packbone-plus-aggeee queries-based aggregator that introduces tokens. (c) Our ImAge only prepends a set aggregation tokens to the patch tokens before a specific block in transformer backbone, making them interact globally via self-attention to achieve implicit aggregation. Notably, these aggregation tokens are simply initialized by the k-means algorithm.
paradigm with queries to learn global information from the patch
Methodology
This section begins with review of the ViT [23] and the self-attention mechanism in it, which serves as the foundation for our ImAge method. Following that, we first present the pipeline our method. Then, we introduce the insertion strategy of our aggregation tokens and their initialization method.
3.1 Preliminary
ViT and its variants have rapidly emerged the preferred backbones for variety of computer
vision tasks [7] 774 73 72 50), owing to their exceptional capacity for modeling global relationships
588. Given an input image size H W. VIT partitions into N HW non-overlapping patches. Each patch is then flattened and linearly projected to create a D-dimensional token ryp A learnable class token FCLS Rº, is prepended to this sequence, and positional embeddings added to encode spatial information, forming the initial input token sequence 2 = [#CLs, .p.... x e R(N+1)xD This sequence is iteratively processed through L transformer encoder blocks. Each block comprises three core components: layer normalization (LN), multi-head self-attention (MHSA), and multi-layer perceptron (MLP). The 1-th block updates the input 21-1 via
2 MHSA (INN(=-1)) + zt-1;
27 = MLP(LN(2)) +
Within the MHSA module, the input sequence undergoes parallel linear transformations to generate h independent sets of queries Q, keys K, and values V, each parameterized by learnable projection matrices. For each attention head, the scaled dot pro-product attention
Attn (Q, K, V) Softmax(QK /VO)/ d= D/h,
computes context-aware similarity scores and dynamically aggregates information across all tokens. This mechanism facilitates rich cross-token interactions, where each token selectively assimilates features from others based on pairwise affinities. The outputs of heads are concatenated integrate multi-subspace representations and then linearly projected again, synthesizing position-wisd updated embeddings z/ that encode global contextual relationships. These properties of VIT indicate its potential to aggregate patch tokens by introducing additional tokens, which we will introduce below.

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3.2 Implicit Aggregation via the Transformer Backbone
After extracting the patch features/tokens via the backbone, there are primarily two ways previous works to obtain robust global descriptors. One is to directly aggregate these patch tokens with common aggregator (e.g., NetVLAD [4] and SALAD (34)) as in Fig. (a). The other uses the queries- based aggregator to learn global information from the patch tokens (e.g.. BoQ [3] and EDTformer (7), as in Fig. (b). However, our ImAge will essentially eliminate the use of aggregators.
An overview of our ImAge is presented in Fig. (c). Unlike existing VPR methods, ImAge removes the explicit aggregator and uses only the backbone network achieve implicit feature aggregation. In this work, we utilize the vision transformer as the backbone, making the first Lı encoder blocks process the patch tokens as usual. After these encoder blocks, a set of M learnable aggregation (agg) tokens, formulated as E RAIxD is introduced and prepended to the other tokens 2, getting new sequence [a. 2]. Then, these combined tokens will be uniformly processed by the subsequent Le encoder blocks and perform global interactions via the internal self-attention mechanism. Specifically, [a, 2] is first linearly transformed to produce the query Q = [Qa. Qel. key K = [Ka, K2), and value follows:
V [Va. Va]- Next, the interactions computed according Eq. 2
AQt K, V) = [Qa: Q=I[Ka, K,] M.. V-]= = 1Qu + +.KK Q.K. V. + Q.K. V3J,
Agg-Agg
Agg-Parch
where we omit the Softmax and scaling operations for simplicity. Based on Eq. 3 it is evident that the self-attention layers within the backbone enable us to achieve two key objectives: 1) Agg tokens can focus on their own features by agg agg attention, thereby enhancing their intrinsic representation capabilities; 2) More importantly, agg tokens can fully learn and capture the global contextual information within the patch tokens by agg-patch attention, thus achieving robust implicit aggregation. Finally, we take the agg tokens from the output of the last encoder block, which are flattened into a vector and L2- 12-normalized to form the final global image representation. It is worth noting that in the previous backbone- taabboon--uussuue suus-aggreggtor paradigm, the global image representation is formed after one- shot aggregation of patch features implemented by the aggregator and is immediately output (without opportunity for refinement). Our method, however, adds agg tokens before a specific block of the transformer backbone. These agg tokens serve global representations, and they are subsequently corrected and refined in subsequent blocks (synchronously with the refinement of patch tokens), rather than being aggregated/yielded all once. This is advantage over the previous paradigm.
Obviously, our ImAge fundamentally diverges from the practices of prompt tuning (aim to fine-tune models) 42 and register tokens (aim to remove artifacts) [18), which discard the newly added tokens finally. Besides, our method also differs from the class token. Our agg tokens have better scalability, along with different insertion strategies and initialization methods, which will be described below.
3.3
The Insertion Strategy of Aggregation Tokens
Our implicit aggregation method provides a robust image representation for VPR in an extremely sim- ple manner. It requires neither explicit aggregators nor any modifications the original backbone. How- ever, where and how to add our agg tokens remains an open issue worthy of further exploration. For instance, previous works such as prompt tuning and DINOv2- register prepend additional tokens to the patch tokens (and class token) before the first transformer block, as
Foids
Farch +
Paich
Folch
4 Tuned
Black
Black
Block
Black
Black
Black
Black
Block
Strategy (a)
Strategy Ours
Strategy (c)
Strategy (6)
Figure 3: Illustration of 4 insertion strategies for agg tokens. (a) Agg tokens are added before all transformer blocks. (b) Agg tokens are added at the junction between frozen and trainable blocks (our strategy). Agg tokens are added a deeper tunable block. (d) Agg tokens added incrementally across multiple blocks rather than all at once.
5

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shown in Fig. 3 (a). Our objective differs from these works, and we no longer follow this way regarding the specific placement of agg tokens. More specifically, there are two reasons for this: 1) Our goal is to aggregate patch tokens with meaningful representations. Since early transformer blocks produce relatively weak features, adding agg tokens at the beginning unnecessary and potentially detrimental to model performance. 2) In the field of VPR, the common practice for model training is to fine-tune only the last few blocks (layers) of the pre-trained model on the VPR dataset, while freezing the previous blocks. If agg tokens are added at the beginning, although most of the shallow and middle blocks are frozen, the added agg tokens need to be tuned. According to the chain rule of back- proppaaaaatttinn [52], the gradients of the parameters in these frozen blocks still need to be calculated, leading to significant GPU memory and computational overhead.
In light of the above considerations, our strategy is to prepend agg tokens only when the patch tokens have acquired sufficient representational capability. A more specific criterion to add the agg tokens at the junction between frozen and trainable transformer blocks, as illustrated in Fig. (b). For example, in the case of the DINOv2 backbone, most previous VPR methods 34 only fine-tune the last four blocks. Accordingly, we prepend the agg tokens to the patch tokens before the fourth-to-last block. Since the preceding blocks are frozen, it indicates that the features output here are general enough. The subsequent blocks are then trained on the VPR dataset to produce features more suitable for the VPR task, so our agg tokens can also learn better task-specific global representations. Additionally, a deeper trainable block, as shown in Fig. (c). The other is to add agg tokens progressively instead of all at once, as shown in Fig. B (d). However, both ways reduce the opportunities for the refinement and correction of image representations, which leads to suboptimal performance. Based on these objective factors, we finally propose the aforementioned strategy (b) for the insertion of agg tokens.
consider two alternative strategies. One is to add agg tokens before
3.4 The Initialization of Aggregation Tokens
The agg tokens are learnable parameters, and their initialization can significantly impact the model performance. Prior to training on VPR datasets, the model is typically pre-trained on large-scale datasets. As result, the patch tokens output by the specific block of such a model already have good representational capabilities. If our agg token is inappropriately initialized and prepended to patch tokens. it will instead cause damage to the representation of patch tokens in the subsequent processing of the MHSA layer. So, proper initialization of the agg token essential.
Fortunately, a similar issue has been discussed in NetVLAD 6). This method determines i cluster centers (and the parameters of the assignment layer) through training. The residual statistics from patch features to cluster centers are used as the global representation. At the beginning, it also requires initializing cluster centers and the soft-assignment layer through the unsupervised k- means algorithm to achieve good performance. Although our ImAge method uses the self-attention mechanism to perform implicit aggregation, its essence can be regarded as each added agg token representing a unique category (but not necessarily corresponding to an object category in human semantics, such as building or vegetation) that helpful to VPR, similar to each cluster in NetVLAD. Therefore, we can learn from NetVL.AD, using the k-means algorithm to perform unsupervised clustering for the initialization of agg tokens. Besides, NetVLAD uses 1.2-normalized cluster centers to initialize parameters (weight w) in the assignment layer. Through our empirical research, the L2-normalized centers can reduce the impact of extreme cases and are more suitable for initializing agg tokens than the original centers, i.e., it is our final method.
4 Experiments
4.1 Datasets and Performance Evaluation
Datasets. We conduct the experiments on several VPR benchmark datasets. These datasets exhibit various challenges, including viewpoint changes, condition variations, and the perceptual aliasing issue. Table [ provides a summary of the main evaluation datasets. MSLS [68] is particularly challenging dataset, which images are taken from urban, suburban, and natural scenes, covering diverse visual changes. Pitts30k [65] extracted from Google Street View, mainly presents severe
variations in viewpoint. Tokyo24/7 [ shows dramatic condition (light) changes. Nordland [62] is
gathered across four seasons with fixed perspective from the front of a train. Morcover, we also use

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the Baidu Mall [6], SPED [16,, Pitts250k 651, St. Lucia 29 supplementary experiments.
and SVOX [12 datasets for a few
Performance Evaluation. We follow the pre- vious work 8 TO using the R (R@N)
Table I: Summary of the main evaluation datasets.
Number
as the evaluation metric for recognition perfor-
Dataset
Description
Database Queries
mance. R@N is the proportion of queries for
Pitts30k
urban, panorama
10,000
6,816
which at least one of the top-N predicted im- ages is within a threshold of ground truth. We set the threshold to 10 frames for Nordland and 25 meters for others, as in this benchmark (10).
MSLS-val
urban, suburban
18,871
740
ASLS-challenge
long-term
38,770
27.092
Tokyo24/7
urban, day/night
75,984
315
Nordland natural, seasonal
27.592
27.592
4.2 Implementation Details
The experiments conducted on the NVIDIA RTX A6000 GPU using PyTorch. We use DINOv2- base-register as the backbone and only fine-tune the last four transformer blocks with the previous layers frozen. The token dimension in backbone is 768, and the number of our aggregation tokens is 8, thus outputting 6 6144-dim global descriptors. The image resolution is 224 x 224 for training and 322x322 for inference, in SALAD 34 We employ the multi-similarity loss (67] for training, with hyperparameters set following the GSV work M. The model is trained using the Adam optimizer with an initial learning rate of 0.00005, halved every 3 epochs. Each training batch contains 120 places, with 4 images per place (i.e., 480 images). Besides, we set the maximum epochs to 20.
4.3 Comparisons with State-of-the-Art Methods
This section shows the experimental comparison of our ImAge with SOTA methods, including 11
single-stage VPR methods: NetVLAD (4), SERS (28), CosPlace (8. Mix (2], EigenPlaces [1]
Cri [49]. SALAD (34), SALAD-CM (33, BoQ [3]. SuperVLAD (51) and EDTformer (37]
as well two-stage VPR methods (Trans VPR [66] and SPR
(00) that leverage local features
for re-ranking. The latest studies, Crica VPR, SALAD, SALAD-CM, BoQ. Sela SuperVLAD, and EDTformer, all use the foundation model DINOv2 as the backbone extract deep features and achieve SOTA results. Our method mainly adopts Cosplace and EigenPlaces construct an extra large-scale dataset (SF-XL) for training. Circa VPR. SALAD, BoQ, and EDTformer are trained on the GSV-Cities dataset, while SALAD-CM combines GSV-Cities and MSLS-train for training. Our work further merges Pitts30k- train, MSLS-train, SF- XL, and GSV-Cities for training, following the process in SelaVPR++ [48]. Table
in experiments. Additionally,
2 presents the comprehensive quantitative results. Moreover, to enable a fairer comparison among three leading aggregation methods (NetVLAD, SALAD, and BoQ) and our ImAge, we conduct consistent comparison using the same setup (backbone, training data, image resolution), as shown Table The experiments using other transformer backbones (ViT and CLIP) are shown in Appendix D
For the comprehensive comparison in Table Compared to existing SOTA methods (e.g. SALAD- CM, BoQ, and EDTformer), our ImAge removes the explicit aggregator and only uses the backbone to obtain robust global descriptors, thus achieving a promising performance. On Pitts30k, a benchmark known for its extreme viewpoint variations, EDTformer and BoQ achieve 93.4% and 93. 93.7% R @@) 1, respectively. In comparison, our ImAge achieves a notable 94. 1% R@1 1, attaining a new level. This indicates that global descriptors produced by our ImAge are highly robust to viewpoint changes. SALAD-CM significantly outperforms other methods on the MSLS dataset, which presents greater challenges due to diverse visual changes and perceptual aliasing. Nevertheless, our ImAge method further advances recognition performance, achieving 94.5% R@I on MSLS-val val and 93.8% R@5 on MSLS-challenge (ranks 1st on the official leaderboard). On Tokyo24/7, which is characterized by severe illumination changes, our ImAge also achieves the best performance with 97.1% 1% R@] 1. In addition to its competitive performance well on natural image datasets suffering from seasonal variations. Specifically, ImAge achieves an almost perfect R@5 (ie.. 99.0%) on Nordland. Overall, compared with other SOTA methods, ImAge delivers substantial performance improvements across diverse scenarios. More importantly, our method no longer relies on dedicated aggregator to obtain such robust global features.
urban and suburban datasets, our ImAge still performs
For the fairer comparison in Table 3 In this comparison, we use the same training dataset (GSV. Cities), backbone COINOv2-base-regise and input image resolution (224x224 in training and
7

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Table 2: Comprehensive comparison existing SOTA VPR methods multiple benehmark datasets. All methods follow the settings of their respective original works, so there are differences in the backbone, training set, image resolution, etc. The best results are highlighted in bold and the second are underlined. i CricaVPR and SuperVIAD use a cross-image encoder to correlate multiple images from the same place achieve better performance Pktts30k. They are not included in the comparison with others (on all datasets).
3ktts3kk
MSLS-val val
MSLS- I R 10
Tokyo24/7
Nordland
Method
Dim
R@1
R@5
R 10
R IR@5R @ 10
R
R@IR@SR 10
R@ @R@5R@ 10
NetVLAD 4
32768
81.9
91.2 93.7
53.1 66.5 71.1
35.1 47.4
51.7
60.6 68.9 74.6
6.4 10.1 12.5
SFRS 228
4096
89.4
94.7 95.9
69.2 80.3 83.1
41.6 52.0
56.3
81.0 92.4
23.9
23.9
28.4
TransVPR
66
/
89.0 94.9 96.2
86.8 91.2 92.4
63.9 74.0
77.5
79.0 82.2 85.1
63.5 68.5
70.2
CosPlace
512
88.4 94.5 95.7
82.8 89.7 92.0
61.4
76.6
81.9 92.7
73.5
79.4
MiXVPR 2
4096
91.5 95.5 96.3
98.0 94.6
65.0
80.6
85.1 91.7 94.3
76.2 86.9 90.3
EigenPlaces
2048
92.5 96.8 97.6
89.1 93.8 95.0
67.4
81.7
93.0 97.5
83.8
88.1
SelaVPR 500
/
92.8 96.8 97.7
90.8 96.4 97.2
87.5
90.6
94.0%6.5 97.5
87.3 93.8 95.6
CricaVPR
44
4096
94.9' 97.3 98.2"
95.4 96.4
69.0
85.7
93.097.5
98.1
90.7 97.6
SuperVLAA
51
3072
95.0* 97.4" 98.2
92.2 96.6 97.4
75.3 86.8
89.9
97.2 98.1
91.0 96.4 97.7
SALAD R4
8448
92.5 96.4 97.5
92.2 96.4 97.0
75.0 88.8
91.3
94.697.5 97.8
89.7 95.5 97.0
SALAD-CM
8448
92.7 96.8 97.9
94.2 97.2 97.4
82.7 91.2 92.7
96.8 97.8
98.0 98.5 99.2
BoQ 3
12288
93.7 97.1 97.9
93.8 96.8 97.0
79.0 90.3
92.0
96.5 97.8 98.4
90.6 96.0 97.5
EDTformer
37
4096
93.4
97.0 97.9
92.6
97.2
78.4 89.8
91.9
97.1
98.1 98.4
88.3
95.3 97.0
ImAge (Ours)
6144
94.1 97.3 98.1
94.5 97.3 98.0
84.5 93.8 95.4
97.1 98.1 98.4
97.7 99.3 99.6
Table 3: Consistent comparison to SOTA VPR aggregation algorithms. * *All methods consistently use the same backbone SUNNOv2-baserregiseey training dataset (GSV-Cities), and image resolution.
Param. in Inference
Pitts30k
MSLS-val
Tokyo24/7
Nordland
Method
Dim
Aggre.
Time (ms)
R@ R R@ 10
R 1 R@5 R 10
R@
R R 10
R@ R 5 R@ 10
NetVLAD
6144
0.012 M
15.0
92.8 96.6 97.8
91.8 96.5
96.6
95.6
98.1 98.7
90.5 96.5
97.8
SALAD*
8448
1.411 M
16.3
92.5 96.6 97.5
92.6 96.6
97.0
95.6 97.5
99.0
86.5 93.6 95.7
BoQ*
12288
8.626 M
16.4
93.1
97.2 98.0
92.8
96.5
97.0
95.2
97.7
98.2
87.0
94.0 95.9
ImAge
6144
O M
14.8
94.0
97.2 98.0
93.0 97.0 97.2
96.2 98.1 98.4
93.2 97.6 98.6
Table 4: Consistent comparison to SOTA VPR aggregation algorithms on supplementary datasets.
* "ll methods consistently use the same backbone -DINOv2-buse-reiistee training dataset (GSV- Cities), and image resolution.
Baidu Mall
SPED
P5ts220k
St. Lucia
SVOX-
SVOX
Method
Dim
R@l
R@s
R@l
R@S
R@l
1 R@s
R@
R@S
R@1
R@s
R@l
R@5
NeLVLAD*
6144
69.8
82.5
91.1
94.9
95.6
98.5
99.9
99.9
97.0
98.9
97.7
99.2
SALAD*
8448
67.3
81.2
90.3
94.6
95.4
98.8
99.9
100
96.1
99.0
97.2
99.4
BoQ*
12288
65.6
79.2
90.3
96.0
95.6
98.9
99.9
100
97.4
99.5
97.4
99.3
ImAge"
6144
70.6
83.8
91.6
95.6
96.5
99.1
99.9
100
97.6
99.4
98.0
99.5
322x322 in inference) for all methods. It is worth mentioning that Fig. has shown some of the results of Table summary, our ImAge achieves the best overall performance on all datasets with the smallest descriptor dimension, the fastest inference speed, and the fewest model parameters. Note that even considering the additional parameters brought by our agg tokens, only 0.006M, i.e., half of NetVLAD (0.07% % of BoQ). This further supports our statement that an elaborately designed aggregator is not indispensable in the transformer era for robust global deseriptors.
Besides, we also conduct the consistent comparison experiments on some supplementary datasets, including Baidu Mall [6], SPED [16], Pitts250k (63). St. Lucia (2)], and SVOX [2]. and the results are shown in Table 4 Compared to the three SOTA explicit aggregation methods, our ImAge achieves the best R@1 1 performance on all supplementary datasets. In particular, on Baidu Mall, which is the only indoor dataset and exhibits distinct visual distribution from the other outdoor datasets, our method achieves the best performance, outperforming NetVLAD, SALAD, and BoQ with 0.8%, 3.3%, and 5.0% absolute R @ improvements, respectively. This demonstrates that the global descriptors produced by our ImAge method through implicit aggregation are not only highly robust against common visual changes but also exhibit superior generalization ability.
8

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Query
ImAge
NetVLAD
SPRS
CosPlace
SelaVPR
SALAD-CM
Lighting changes
Perceprual
aliasing
Viewpoint
changes
aliasing
Scasonal changes
aliasing
Weather
changes
Viewpuint
changes
Figure 4: Qualitative results. In these four challenging scenarios (involving dynamic objects, severe viewpoint variations, condition changes, etc.), our proposed ImAge method consistently retrieves the correct results from the database, while other methods all return the wrong images.
Fig. presents qualitative retrieval results, where the proposed ImAge consistently demonstrates high robustness various extreme scenes. For example, the first three cases exhibit severe lighting changes, viewpoint variants, and seasonal transitions, respectively. Other methods often retrieve visually similar but actually incorrect results due perceptual aliasing. However, our ImAge effectively addresses these challenges in VPR and successfully returns the right results.
4.4 Ablation Studies
In this section, we conduct series of ablation studies on our ImAge. We uniformly use the DINOv2-base-registe backbone and train models GSV-Cities with the batch size set 120 (as the experiment in Table 3 Unless stated otherwise, we only fine-tune the last four transformer blocks.
Effect of tokens insertion strategy. In Section 3.3 discussed several strategies for adding agg tokens and proposed the optimal strategy. To validate its effectiveness, we conduct an ablation to compare different strategies. To be fair, consistently add agg tokens. Strategy (a) and (ā) both add agg tokens before the first transformer block. The only difference is that all transformer blocks in (é) are trainable. Strategy (b) is optimal strategy. Strategy (c) introduces agg tokens before the penultimate block. Strategy (d) progressively adds agg tokens before each of the last four blocks. Results are presented in Table 5 Among them, (a) performs the worst, because the early frozen transformer blocks produce weak and less informative features for VPR, harming the agg tokens effectively capture meaningful global information. The issue mitigated in (a), which further confirms our hypothesis (i.e., adding agg tokens before the first trainable blocks). However, (a) trains all blocks, which incurs lot of computational overhead and damages the excellent transferability of foundation models, thus failing to get optimal results. When fine-tuning only the last four transformer blocks, our proposed strategy (b) consistently outperforms
alternatives on all datasets by large margin. This is because the last four tunable blocks can produce more suitable features for the VPR task, so our agg tokens can fully learn task- specifc global representations. Although (c) and (d) also show relatively competitive performance, the late or gradual addition of agg tokens provides fewer opportunities to interact with patch features, thus limiting their ability learn better representations.
Effect of aggregation tokens initialization. To validate the effectiveness of our proposed initializa- tion methods for agg tokens, we conduct an ablation study using four initialization strategies: zero initialization (i.e., no initialization), normal distribution initialization (commonly used for the class token register tokens initialization (18), vanilla cluster centers (yielded by k-means) initialization. and 1-2-nommaized cluster centers initialization (i.e., ours). We consistently use 8 agg tokens and prepend them to the patch tokens before the fourth-to- transformer block. The experimental results are presented in Table Zero initialization produces uniform representations at the beginning, limiting (even harming) interaction between agg tokens and patch features, and hindering global context modeling. In contrast, normal initialization provides a better inductive bias during early training and introduces slight randomness into the agg tokens, which helps break symmetry to get better performance. However, both initialization methods lack any visual prior, forcing the agg tokens

---

Table 5: Comparison of different insertion strate- gies for agg tokens. The strategy (b) is ours.
Table 6: Comparison of different initializations for agg tokens. The centers-I.2N is ours.
Pitts30k
MSLS-val
Nordland
Method
Pkk
MSL.S-vll
Nordland
RΦ
R@5
R@1
R@5
R@1
R@5
Method
R@1
R 5
R@ R@5
R@ R@5
Strategy (a)
88.5
94.1
83.6
90.9
40.4
56.2
Strategy (a)
92.6
96.9
92.0
96.6
89.0
95.6
zero
92.1 96.6
89.6
95.1
68.9 82.7
normal_ ddittrbb
92.9 96.9
92.0 96.8
88.6 95.3
Strategy (b)
94.0
97.2
93.0
97.0
93.2
97.6
centers
93.5
96.9
92.6 96.9
91.7 97.0
Strategy (c)
93.2
97.1
92.2
96.5
88.1
95.0
centers-L.2N
94.0
97.2
93.0
97.0
93.2 97.6
Stratogy (d)
93.3
97.1
92.4
96.6
90.3
96.4
to learn the patterns relevant to VPR from scratch, which constrains their final performance. Initial- izing agg tokens with cluster centers can be viewed as injecting a data-driven prior. These centers, obtained via unsupervised clustering of descriptors from randomly sampled training images, tend to capture common visual patterns. Such initialization can facilitate agg tokens to learn meaningful global information and diminish useless elements. Moreover, 1.2-normalized cluster centers offer more robust initializations for agg tokens by mitigating the influence of outliers, thereby achieving the optimal performance all datasets.
Effect of the number of aggregation tokens. In this subsection, investigate the impact of the number of added agg tokens (and use the class token, i.e., cls, baseline). The agg to- kens are all added before the fourth-to-last block, and the results are in Table Even with single agg token, ImAge demonstrates a clear advan- tage over the class token with the same dimen- sionality, notably achieving an 1 11.3% absolute R @@1 improvement on Nordland. This proves the differences and advantages of our method compared with directly using the class token, as well as the excellent performance of our method with low- liw-dimesiional descriptors. Furthermore, performance consistently improves as the number of agg tokens increases. with the best results obtained using 8 agg tokens. This is because a moderate increase of agg tokens enables more sufficient interaction and finer aggregation from the patch tokens. However, when the number of agg tokens becomes excessively large (e.g., 64), a noticeable decline is observed. This may be attributed to the global nature of self-attention, where an excessive number of agg tokens can interfere with the contextual information of patch tokens, thereby indirectly degrading their own representational capability. Thus, adding 8 agg tokens a promising choice overall.
Table 7: Comparison with the ImAge ablated ver-
sions with different numbers of aggregation tokens.
Pitt33kk
MSLS-val
Nordland
Number
R@l
R@S
R@l
R@s
R@l
R@s
cls
91.8
96.5
89.1
95.3
63.5
79.0
1
92.2
96.6
90.7
95.4
74.8
87.0
4
93.4
97.0
92.3
96.6
89.6
96.1
8
94.0
97.2
93.0
97.0
93.2
97.6
16
93.7
97.2
92.8
96.9
92.2
97.2
32
93.1
96.9
92.6
96.8
90.3
96.2
64
92.8
96.8
92.2
96.5
85.4
93.0
5 Conclusions
In this paper, we presented ImAge, an innovative paradigm that explores implicit aggregation with a transformer to produce robust global image representation for VPR. Our method only adds some aggregation tokens and leverages the inherent self-attention of the transformer backbone to implicitly aggregate patch features. It overcomes the limitations of the previous packbone-plus-aggres paradigm in an extremely simple manner, which neither modifies the original backbone nor requires an extra aggregator. Moreover, we propose an aggregation token insertion strategy and a token initialization method for our ImAge method to further improve the performance and efficiency. Experimental results show that ImAge obviously outperforms the latest explicit aggregation methods with higher efficiency under the same setup and achieves SOTA results on common VPR datasets.
Acknowledgments and Disclosure of Funding
This work was supported by the National Key R&D Program of China (2022YFB4400140004770
SSTIC Grant (KJZD202309231 15106012, 2, 1 149116032, GJHZ22240218 3604008), National Natural Science Foundation of China (62402252, 62536003), and Guangdong High-Leevel
Talent Programme (2024TQ08X28)).
10

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A Broader Impacts
Visual place recognition plays an important role in several applications, including autonomous driving. augmented reality, and robot localization. Our work proposes an implicit aggregation method to produce robust image representation for VPR with the transformer- based backbone and shows SOTA performance. While our exploration on VPR remains fundamental and application-agma potential utilization of VPR technology for intrusive surveillance and social media monitoring raises some privacy issues. It is crucial to prevent the misuse of VPR research for detrimental purposes.
the
B Limitations and Future Work
While our study provides a novel insight (i.e., implicit aggregation without an explicit aggregator) into achieving robust global image representation for VPR, we acknowledge three limitations of our work: 1) Although our ImAge method demonstrates universality for the transformer-based models, compared to the foundation models pre-trained on the massive dataset (e.g., DINOv2 pre- trained on the LVD-142M, dataset (55), the performance improvement is less pronounced when using backbones without pre-training
sufficiently massive data (e.g., the VIT pre-trained only
on ImageNet [9].. This will be shown in Appendix D However, this also suggests that with the advancement of increasingly powerful foundation models, the superiority of approach compared to existing VPR methods may become more prominent. 2) The proposed method may not be good choice when want to keep the backbone frozen (like AnyLoc (39] or when the backbone is extremely expensive to fine-tune. However, it is also worth noting that we only fine-tune the last few blocks of the transformer backbone in most cases, which is relatively cheap. 3) Although this work focuses on the VPR task, we believe that the proposed ImAge is broadly applicable to a wide range of image (or other modalities) retrieval tasks. The potential of our ImAge method for more applications in the machine learning community needs to be further explored through more experiments in future work.
C More Details about the Relations & Differences to Other Methods
Our ImAge design draws inspiration from prior works, particularly DINOv2-register [8] and prompt tuning [42]. Both approaches introduce additional tokens to the transformer backbone before the first encoder block. However, their objectives and usage differ fundamentally from ours. Prompt Tuning aims to adapt a frozen model to downstream tasks by learning a set of prompt tokens in a parameter-efficient manner, while these tokens are typically excluded from the final representation. DINOv2-register introduces additional register tokens to mitigate artifacts in the feature maps. Although the study shows that these registers may capture certain global information, they are ultimately discarded, and only the patch and class tokens are used for downstream tasks. In contrast, our ImAge method introduces the aggregation (agg) tokens before particular transformer block, and utilizes agg tokens from the output of the last block as the final global image representation. which provides a reverse perspective compared to these approaches. In addition, while the class token within the transformer backbone is sometimes used as global representation, our ImAge differs in several key aspects and demonstrates superior scalability. First, the class token is typically introduced at the beginning of the transformer and starts to learn from shallow features, which may limit its flexibility and make it difficult fully capture task-specific complex semantics. Second, the class token is a single fixed embedding, which inherently restricts its representational capacity. Although it may suffice for relatively simple classification tasks, it often proves inadequate for more complex scenarios requiring richer and more flexible representations. In contrast, our ImAge introduces agg tokens with customizable positions and quantities, allowing them to fully learn task-specific global features. Moreover, we also design tokens initialization method based on the k-means algorithm, which is significantly different from other works. Additionally, among existing VPR methods, BoQ 3 also offers some valuable insights for our work. However, it elaborately designs an explicit aggregator consisting of additional encoder blocks and cross-attention layers, which aggregates global information from patch tokens into set of extra learnable queries. In contrast, we focus on the transformer backbone itself and make use of the inherent self-attention mechanism. Our study reveals a new insight: the aggregation function, previously implemented through an exquisitely designed aggregator, already appears naturally in the transformer backbone. We demonstrate that, by adding just some additional tokens, we can fully develop this implicit and progressive aggregation behavior.
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Table 8: Results of NetVLAD and our ImAge using CLIP (base version, only vision encoder) and ViT (base version) as backbone. All models are trained on the GSV-Cities dataset with the batch size equal to 120. The learning rate is 0.00006 for the CLiP-based model and 0.0003 for the ViT-based model. For ViT, the last two blocks are directly truncated and all other blocks are trainable, as in For CLIP, we only train the last 6 blocks with the previous layers frozen. All methods produce 768*8-dimensions descriptors, L.., 8 clusters for NetVLAD and 8 aggregation tokens for ImAge, the same in the main paper.
10 51
Pktts30k
-SLl-val
Nordland
Method
R@1 1
R@5
R 10
R@1
R@5
R@
10
R@
R@5
R@ 10
CLIP-NetVLAD
90.6
95.7
97.2
87.2
94.1
94.6
60.6
74.6
80.2
CLIP-ImAge (Ours)
91.2
95.6
96.9
88.2
94.1
95.5
61.0
74.6
80.1
ViT-NetVLAD
90.1
95.3
96.4
82.4
90.7
93.0
52.1
67.6
74.1
ViT-ImAge (Ours)
90.3
96.1
97.3
86.2
92.2
93.8
53.3
69.3
75.6
Table 9: Results of CPR and the CricaVPR boosted by ImAge (i.e. CricaVPR+
Pius30k
MSLS-val
Nordland
Method
R@i
R@s
RØ
10
R@
RØS
R@ 10
R@1
R@5
R 10
CricaVPR
94,9
97.3
98.2
90.0
95.4
96.4
90.7
96.3
97.6
CrimaVPR+ImAge
94.9
97.5
98.2
92.0
97.2
97.3
94.1
97.9
98.7
D Comparison to NetVLAD Using Other Transformer Backbones
In the main paper, conduct experiments using the DINOv2 backbone validate the effectiveness of our method. Notably, DINOv2 is a foundation model hased on the ViT architecture and pre-trained on the large-scale curated LVD-142M dataset. However, our method is also applicable transformer models. To this end, we conduct additional experiments using the CLIP [57] model and VIT model pre-trained only ImageNet. The results are shown in Table g We observe that our proposed method, ImAge, consistently achieves higher R@ performance across all datasets compared to the explicit aggregation method NetVLAD. However, the performance gains of ImAge are less pronounced (except for ViT-ImAge on MSLS-val) compared to using DINOv2-base-reiiste as the backbone. This observation aligns with the prior study [40] which suggests that foundation models pre- are-trained on large-scale datasets (significantly larger than ImageNet) are more capable of utilizing additional tokens to capture global information. Moreover, using CLIP as the backbone yields significantly less improvement than DINOv2. Although CLIP is widely used foundation model (i.e., a vision-language model), its pre-training data and objectives differ considerably from those of the VPR task, making it not promising choice. This is consistent with the prior work AnyLoc (39] which suggests that CLIP performs significantly worse than DINOv2 in outdoor VPR scenarios.
other
E
Improving Other VPR Methods with ImAge
Since our ImAge is general image representation method for VPR, it can not only be implemented based on different transformer backbones, but also can be combined with some other VPR methods to improve their performance. This section uses the CricaVPR [49] method experiments, and the results are shown in Table It can be seen that our method significantly improves the performance.
example conduct
The GPU Memory Usage and Computational Efficiency in Training
Our method does not add agg tokens before the first block of the transformer backbone, which can significantly reduce GPU memory usage and computational burden. Here, we not only compare our method with adding tokens before the first block, but also use NetVLAD and SALAD as baselines. The results are shown in Table 10 Our method has significant advantages over adding tokens before the first block in terms of GPU memory usage and training time, and also outperforms NetVLAD and SALAD.
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Table 10: The comparison of training GPU memory usage and training time.
Method
Training GPU Memory (GB)
Training Time/Epoch (min)
NetVLAD
17.54
9,93
SALAD
21.81
9.98
Adding tokens before 1st block
34.00
15.12
ImAge (Ours)
16.73
9.87
G
The Attention Visualization of Aggregation Tokens
Here we provide the visualization of attention weights of our agg tokens to other patch tokens, as in Fig. 5 This vividly demonstrates that our agg tokens can effectively focus objects beneficial for VPR (e.g., buildings and vegetation) while ignoring irrelevant or even detrimental elements (e.g., sky and moving vehicles). Additionally, consistent attention on key objects under significant illumination and seasonal changes, indicating high robustness. 2) The attention critical objects is sparse rather than uniform, suggesting that typically only the most distinctive features need to be considered for VPR. Even for buildings, there no need to focus on (aggregate) their full area. 3) Some agg tokens foeus on both buildings and vegetation, and there are also multiple tokens that focus on buildings. Therefore, there is not a one-to-one correspondence between agg tokens and human-defined object categories.
can observe that: 1) Our method maintains
H Additional Qualitative Results and Failure Cases
In this section, provide additional qualitative results (i.e., visual examples) as supplement for Fig. 4 in the main paper. As shown in Fig. 6 our ImAge method demonstrates exceptional robustness in retrieving correct database images across various challenging scenarios, including saapooaa/viewpoon///igeee
variations and occlusions. In contrast to other methods that fail to distinguish critical landmarks or are misled by superficial similarities. the proposed ImAge accurately captures key features (e.g., building textures, positional relationships) to identify right matches.
Moreover, Fig. 7 iillustrates some representative failure cases. While method achieves rela- tively close retrievals (within 50 meters) in ambiguous natural scenes without distinct landmarks, it occasionally exceeds the predefined threshold (i.e., 25 meters) due to geographic proximity but insufficient visual discriminability. The fourth example, which is the most challenging, involves nighttime images with over-exposure and motion blur, where all methods (including ours) even fail to meet the 50-meter criterion, highlighting persistent challenges in low-quality visual conditions. These results underscore both the advancements of our approach and the remaining difficulties in VPR, which may require increasing the geographical density of image collection for the database to solve. Additionally, for the last two samples, SelaVPR based on local feature re-ranking obtains the correct results, while other methods (including ours) all fail. This points to possible way to further enhance the robustness of our approach in the future.
I More Details about Datasets
The testing datasets used in our experiments, including Pitts30k, Pitt2500,, Tokyo24/7, Nordland,
SPED, St. Lucia, and SVOX, are organized following the Visual Geo-localization (VG) benchmark
10. Notably, we use the official version MSLS dataset as in previous work 68] 2 49
3 R4 This
version of MSL-Sval only consists of 740 query images, which is different from the version in the VG benchmark [0]. In addition, there are also several versions of the Nordland dataset in the VPR task. In our experiments,
use the version in the VG benchmark (10), which employs the summer images as the database and the winter images as queries, each containing 27592 images. Baidu Mall (6 is well-known indoor dataset for image-based localization. All images are collected at shopping mall that is over 5000 square meters with many challenging elements, such as transparent windows, reflective materials, repetitive structures, dynamic pedestrians, etc.
the comprehensive comparison (i.e., Table in main paper) with other SOTA methods, we merge Pitts30k-train, MSLSS-rain, SF-XI, and GSV-Cities for training, following the approach in SelaVPR++ [48) Specifically, we process datasets other than GSV-Cities to divide places into finite number of categories, thus facilitating fully supervised training with the multi-similarin loss [67].
Moreover,
25

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(a) Input image
(b) Attention weights of single aggregation token
(c) Merged attention
Figure 5: The visualization of the attention weights of our agg tokens to patch tokens. The first column (a) represents the input images. The middle 2-5 columns (b) separately display the attention weights of a single agg token to all patch tokens (reshaped to restore spatial position), meaning each image shows the attention of only one agg token. The last column (c) shows the merged attention of all 8 agg tokens. The first five examples (i.e., five rows) show five different places, with buildings, vegetation, and dynamic interference. While different agg tokens attend to distinet regions (or objects) in the images, they consistently focus on stable and discriminative areas (e.g., buildings and vegetation), while largely ignoring variable elements (e.g., cars). The sixth and seventh examples show two images taken at the same place in different seasons. Our agg tokens can consistently focus on buildings (and some discriminative regions where the terrain and railroad tracks change). The last two examples demonstrate that agg tokens can consistently focus on buildings and landmarks even after undergoing severe lighting changes.
More Details about Compared Methods
In the main paper, we compare our method with several other approaches and briefly introduce them. Here, we provide more details about them (for the results in Table 2)
NetVLAD 4 and SFRS 28 both consist of a VGG16 backbone and NetVLAD aggregator, and
use 30k as the training dataset. The latter employs self- self-supervised image-to-region similarities to mine hard positive samples for training a more robust model. In our experiments, we use their PoTorch implementations for comparison.
26

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Query
ImAge (Ouns)
NetVLAD
SFRS
CosPlace
SelaVPR
SALAD-CM
Scisonal
changes
Perceptual
aliasing
changes
Perceptual
Lighting changes
Perceptual
aliasing
Ooclusions
Perceptual
aliasing
Figure 6: Qualitative results. In four challenging groups of examples (covering seasonal changes, viewpoint variations, lighting changes, occlusions, etc.), our ImAge successfully retrieves the correct database images, while other methods fail. In the first group, most other methods return incorrect images with landscapes absent from the query image (e.g., lakes, cliffs, and hills) or railway tracks of contradicting shapes. The examples in the second group exhibit significant viewpoint variations, where our ImAge consistently gets the right results and demonstrates high robustness. In contrast, other methods return images that appear similar in viewpoint but are actually wrong. Still, they cannot distinguish the critical difference of the landmarks (e.g.. the texture of the buildings and their positional relationship).
for the third group, the dim nature of the query image likely interferes with the judgment of the other approaches, resulting in low-luminosity images with different buildings. Dynamic objects like cars in the first example query of this group are also misleading. Nevertheless, our method successfully caught the key features (e.g., the texture of buildings). The final group shows a complex query with severe occlusions by a colossal tree. is so difficult that these methods except ours have crashed, returning perceptually similar but wrong images that also extensively covered (by darkness, brightness, and trees). In summary, our ImAge method demonstrates unparalleled capacity to recognize the truly identical place against various perceptual variations.
CosPlace 8 and EigenPlaces 11 both frame VPR training as a classification task and use the the official model based the VGG16
SF-XL dataset train their models. For Cosplace, backbone (with the 512-dim output feature) for testing. For HigenPlaces, we utilize its official implementation and the best configuration based on the ResNet50 backbone to output 2048-dim global descriptors for comparison.
MixVPR 2 aggregates the deep features using the multi-layer perceptrons and trains the model with multi-similarity loss [67] on the GSV-Cities [1] dataset. We apply its best-performing configuration (ResNet50 with 4096-dim output features) for comparison.
Cri 44 SuperVLAD 51 SALAD 34 BoQ
3 and EDTformer 37
all use
foundation model DINOv2-base 55 as the backbone to extract deep features, and train their models on GSV-Cities with the multi-similarity loss. In the comparison experiments, we consistently use their official implementations and the best configurations.
27

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Query
ImAge (Ours)
NetVLAD
SFRS
CosPlace
SelnVPR
SALAD-CM
Pinss
MSLSS
Nordland
Tokyo24/7
Pits30k
MSLS
Figure 7: Failure cases. In the first three examples, our method retrieves database images that are geographically close to the query images. However, the distance (radius) between these retrieved images and the query images exceeds the predefined threshold (i.e., 25 meters), although remains below 50 meters. These cases (partially correct) are labeled in yellow. For the first two cases, ImAge tolerably retrieves results with distances of 33.11 meters and 27.32 meters, while other methods even fail to find an image within 50 meters. In the third example, the images are captured in natural scenes without discriminative landmarks. Nonetheless, ImAge can effectively exclude incorrect answers involving ponds and rivers, while some other methods fail to do so. The distance for this retrieval is a fair 35.70 meters, compared to other methods ranging from 161 10 meters to 4592.90 meters. In the fourth challenging example, all methods, including ours, fail to get answer within 50 meters. This challenge arises from the complex lighting conditions at night, where over-exposure in bright areas, such lights, affects the overall texture and the visibility of landmark details. For the last two challenging cases (involving large changes in viewpoint), all methods (including ours) that rely solely on global features for retrieval fail. Notably, SeR,VPR, which is based on local features re-ranking, yields the right results. This provides a potential direction for further improving the accuracy of our method. In short, some challenges for current VPR methods remain, despite our method moving step forward from others.
SALAD-CM 33 is an improvement of SALAD. This work analyzes the Geographic Distance Sensitivity of VPR embeddings and proposes a novel mining strategy to address it. Moreover, SALAD- CM first trains the model using both the GSV-Cities and MSL.S datasets for better performance. In the comparison experiments, we follow its official implementation.
The rest TransVPR [666 and SPR (50) are two-stage VPR methods. These works provide two models: one trained for testing on urban datasets (e.g., Pitts30k and Tokyo24/7), and another trained for testing on datasets that may contain suburban and natural scenes (e.g., MSLS and Nordland). We follow the usage in their original paper for comparison experiments.
K
The Snapshot of MSLS Leaderboard
Fig. g is the snapshot the MSLS place recognition challenge 68 leaderboard at the time of submission, and our ImAge method ranks 1st.
28

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Results
Uner
Entriss
Date of Last Eatry
receligh
ImAge4VPR
05/11/25
(1)
SelaVPRilusspis
3
01/31/25
0.94 (1)
anonymoue4466
03/02/25
amarailbey
07/07/24
0.90
04/17/24
0.90
SKjosan
07/04//4
an ancrymous 123
07/08/24 9
0.90
ningguucaao
maggius
06/05/94
0.88 (6)
36/05/24
11 loquierdo
11/15/23
0.09
ancoymouuds
09/17/23 3
06/12/24
14
12/19/33
0.88
(10)
Figure The snapshot of MSLS place recognition challenge leaderboard. Our ImAge method (named "ImAge4VPR" for double-blind policy) ranks 1st at the time of submission.
29


Reviews:
[{"review_id": "W5AJqoBomV", "reviewer": "Reviewer_WgNP", "summary": "This paper proposes a new approach to adapt vision transformers for visual place recognition. Specifically, the novelty focuses on the pooling/aggregation mechanism.\nRather than using an additional separate pooling mechanism on top of the backbone, this work proposes to use additional \"aggregation\" tokens that use the same self-attention mechanism already in place within the transformer. The output embeddings of these tokens can then be used as image descriptors for place recognition.\nThis approach outperforms previous visual place recognition approaches.", "questions": "1. Could the authors please address my concerns on misleading statements in the paper detailed in my comment on quality #4? These include how the solution tackles claimed limitations of two-stage approaches, clarifications on the description of the method being “aggregator-free” and having “0 aggregator parameters”, experimental details in the caption of table 2, and the conclusions drawn from figures 4, 5 and 6.\n2. Can the authors please clarify how the clustering for initialisation works? Specifically, which features are extracted and used for clustering? Is this a limitation for transferring to new settings?\n3. Could the authors please discuss potential limitations from moving away from a two-stage approach? I would expect that this would lead to more challenges if one wants to keep the backbone frozen (e.g., to get good generalisation in new domains, as discussed in [40], or if the backbone is extremely expensive to fine-tune).\n4. Can the authors please expand more on the statement from line 244-245: \"each added agg token representing an object category\". It is unclear to me why we would be able to make that assumption, and why tokens would end up with separated categories to represent.\n5.  Can the authors please clarify why the proposed aggregation tokens are not classified as \"output-oriented tokens\"? They seem to follow the same purpose as class tokens, just with different design choices on location and initialisation.", "limitations": "The authors discuss some limitations in the appendix, but not in the main paper. Moreover, some additional limitations would be interesting to discuss (see my question 3).", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "(+) = strength, (-) = weakness\n\n**Quality**:\n1. (+) The solution is technically sound, and the design choices are well validated through ablation studies.\n2. (+) The results appear strong compared to existing methods and cover multiple datasets.\n3. (+) The approach remains very simple by using an existing mechanism in transformer models, yet is very powerful.\n4. (-) I find the way some motivations, properties, and results are presented to be potentially misleading:\n- 4.1. While I agree in the value of this work, I struggle to understand how it resolves the claimed issues about standard two-stage approaches. The authors claim that two-stage approaches may not achieve global optimality, and that specific approaches such as NetVLAD might suffer from cluster centres learned on the training set not being suitable for the test set. However, two-stage approaches can also be fully differentiable, and I do not see why ImAge would be more likely to achieve global optimality. Moreover, I also do not see any reason or results indicating that ImAge would not also suffer from differences between train and test settings.\n- 4.2. The claims on the approach being aggregator-free are a bit misleading. While I agree this could be considered an implicit aggregation mechanism, as it is already part of the original model, I still believe that calling the method \"aggregator-free\" is not completely correct and confusing. Similarly, it doesn't really have 0 aggregator parameters, as the shared attention weights are fine-tuned to learn the aggregation. \n- 4.3. While the results in Table 2 are interesting, they are not based on a fair comparison, as mentioned in the text. I believe this should be made clearer by specifying it in the caption as well.\n- 4.4. The results of figures 4, 5, and 6 do not seem very representative of overall performance. The baseline results on the datasets presented were still strong, and usually just slightly below the performance of ImAge, and it is not specified how these examples were selected. As such, I believe these figures might only be highlighting cherry-picked samples where ImAge succeeded and the baselines did not, but I do not believe we can make many conclusions based on this. If the samples are actually randomly chosen, it should be clarified. Based on the overall metrics, there are likely cases where ImAge also fails and some baselines succeed. I would expect a more complete quantitative study on the performance with different types of scenarios (e.g., dynamic objects, viewpoint variations, etc.) if the authors want to claim that ImAge consistently retrieves the correct results where others don't (Fig. 4 caption).\n- 4.5. In Table 2, CricaVPR and SuperVLAD are not included in the bold/underlined results only on Pitts30k, but it would be more consistent to either not include them on any datasets, or include them on all.\n\n**Clarity**: \n1. (+) The introduction of the idea of the paper relative to existing works is very clear, and the figures illustrating the approach are easy to understand.\n2. (-) The text in the paper is very dense, with figure captions sometimes getting really close to the text (e.g., Fig. 3). This negatively affects how easy it is to read the paper, and breaking down long paragraphs with more structure would help improve clarity.\n3. (-) The details of the k-means clustering used for initialising tokens remain unclear to me (see question 2).\n\n**Significance**:\n1. (+) I believe that the results from this paper can affect what future research on the topic of visual place recognition will focus on, potentially shifting away from designing new aggregation mechanisms and focusing on best adapting existing pre-trained models and relying on their existing aggregation mechanisms.\n\n**Originality**: \n1. (+) While the technical solution proposed relies on existing mechanisms, the strength of the results combined with the simplicity of the approach makes this work original enough in the context of the visual place recognition literature.", "was_revised": true, "final_justification": "The rebuttal has addressed my issues with presentation and clarity.\n\nWhile relying on existing mechanisms, I believe the authors validate the specific design of their approach, and demonstrate strong results that might influence future research on visual place recognition. \nAs such, I find this work to be valuable and would recommend that it be accepted.", "rebuttal": "Thanks for your positive recommendation and insightful comments. The following are Responses to the Questions.\n\n**Q1 The comment on quality #4**\n\n-\tFor quality 4.1: Sorry to confuse you. **a)** The claim of “global optimality” is indeed inaccurate because other methods like NetVLAD can also be fully differentiable. We will delete this claim and directly state that “our method provides a novel perspective different from the previous paradigm, unifying feature extraction and aggregation into a more cohesive framework”. Unlike previous methods where the backbone is only used for patch feature extraction, our global features based on implicit aggregation can be further corrected and refined synchronously with the patch token extraction process in the backbone, achieving better performance effectively and efficiently. **b)** Regarding “NetVLAD might suffer from inappropriate cluster centres”, we mainly want to illustrate that it is difficult to artificially design a perfect aggregator, which motivates us to propose a  solution that does not require the explicit design of additional aggregators. Since domain generalization is not the main focus of our work, we will remove this statement to avoid causing confusion.\n\n-\tFor quality 4.2: We agree with you. \"aggregator-free\" and “0 aggregator parameters” are a bit confusing. We will provide further explanations in these places -- there is no **extra explicit** aggregator, and our aggregation process is completed in the original backbone.\n\n-\tFor quality 4.3: We will add clearer explanations in the table caption according to your suggestion.\n\n-\tFor quality 4.4: You are right. These examples are cherry-picked. In fact, this is a common practice in many great VPR works, e.g., in the main paper or supplementary material of the CosPlace, EigenPlaces, StructVPR, and CricaVPR works. To demonstrate the performance of methods as reasonably as possible, we have included a wide range of scenarios (viewpoint and/or condition changes, dynamic objects, etc.) in Figures 4 and 5. Additionally, Figure 6 presents the failure cases of our approach. Following your suggestion, we will add some cases where ImAge fails while some baselines succeed and provide further discussion.\n\n-\tFor quality 4.5: We will revise it according to your suggestion.\n\n**Q2. How clustering works**\n\nThe clustering process is basically the same as the k-means clustering in the NetVLAD implementation of the VG Benchmark work [1]. We randomly sample some training images (e.g., 500 images), and use the pre-trained backbone to extract patch tokens (features) from these images. Then we randomly sample some patch tokens of each image (e.g., 100 tokens per image). Finally, we perform k-means clustering for the obtained (500*100) patch tokens. The transferring to the new settings is very easy. This is demonstrated by the widely used VG benchmark work (ours is the same).\n\n**Q3. Potential limitations**\n\nThanks for your insightful comments. The proposed method is indeed not a good choice when we want to keep the backbone frozen or when the backbone is extremely expensive to fine-tune. We will discuss these two points in the final paper. However, it is worth noting that we only fine-tune the last few blocks of the transformer backbone, which is relatively cheap. This can be seen from the response to the Q1 of `Reviewer VZG8`. We can also further reduce the fine-tuning cost by reducing the number of blocks fine-tuned, but this may cause performance degradation.\n\n**Q4. Why do tokens represent separate categories?**\n\nSorry to confuse you. We guess the word \"object category\" misleads you. We originally intended to use the analogy of human-level semantic classification to visually illustrate the division of multiple agg tokens, which is indeed not rigorous enough. The supplementary materials we submitted (a PDF in the zip file) have shown the attention visualization of agg tokens to image patches, which provides vivid and interesting qualitative results. The highlight areas of attention for different agg tokens are indeed different, that is, each token is responsible for representing different categories. In fact, both the initial k-means-based initialization and subsequent training will make different tokens represent different categories. If they represent the same category, multiple tokens will be redundant. Only when different tokens represent different categories can the representation ability be stronger. However, it does not mean that a token exactly corresponds to a human semantic object (e.g., the building or vegetation). We can see that some tokens contain both buildings and vegetation, and there are also multiple tokens that focus on buildings. To avoid misleading, we think a reasonable expression could be \"each added agg token representing a unique category (but not exactly an object category in human semantics, such as building or vegetation)\".\n\n**Q5. Are aggregation tokens \"output-oriented tokens\"**\n\nYour judgment is correct—the proposed aggregation tokens are indeed \"output-oriented tokens.\" In fact, we explicitly mentioned this point in the initial manuscript, but it was deleted during the paper’s streamlining process. We only stated that our work was inspired by the DINOv2-register work, not that our method belongs to that category. Since this has caused ambiguity, we will clearly state in the final paper that our method falls into the \"output-oriented tokens\" category.\n\n**Clarity. The paper is very dense**\n\nWe will make revisions according to your suggestions.\n\n**Thanks again for your valuable comments and suggestions. Please let us know if you have any further concerns. We'd be very glad to address them.**\n\n**`Reference`**\n\n[1] Gabriele Berton, et al. Deep visual geo-localization benchmark. CVPR 2022."}, {"review_id": "bbWmV9AakB", "reviewer": "Reviewer_VZG8", "summary": "The paper introduces ImAge, an innovative implicit aggregation paradigm for visual place recognition (VPR) that leverages the self-attention mechanism of transformers to eliminate the need for explicit aggregators. \nThis approach addresses critical limitations of traditional \"backbone+aggregator\" architectures, such as suboptimal two-stage optimization and loss of positional information. \n\nThe core innovation lies in:\n1. Introducing learnable aggregation tokens that interact with patch tokens via self-attention within transformer blocks.\n2. Proposing an optimal token insertion strategy and initialization method to enhance representation robustness.\n3. Demonstrating state-of-the-art (SOTA) performance across diverse VPR benchmarks with higher efficiency.", "questions": "1. Plz provide the memory and computational efficiency comparison results.\n2. Plz provide more ablation studies on ViT-based VPR methods to show the additional performance gains and show more significance of this work in VPR field.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Paper writing is good and clear to show the motivation and comparison.\n2. Thorough ablation studies and fair comparisons validate the method's robustness and efficiency. The approach ranks 1st on the MSLS challenge leaderboard (Fig. 7), demonstrating real-world applicability.\n3. The visualization of the attention weights of agg tokens to patch tokens shows the convincing training proces.\n\nWeakness:\n1. No GPU memory and computational efficiency comparison has been shown in quantitative experiments while the authors repeatedly stressed “leading to significant GPU memory and computational overhead”.\n2. Although this paper has shown comparison of different aggregators based on standard transformer-backbones ViT and DINO-V2，I am curious about the results of transformer-based VPR methods with agg tokens. For example, R2Former.\n3. Such agg tokens are not novel enough for transformer/VLM era. Of course, it's kind of novel for VPR.", "was_revised": true, "final_justification": "The comments from the authors resolve my concerns about W1 and W2. I will finally give \"accept\".", "rebuttal": "Thanks for your positive recommendation and valuable suggestions. The following are Responses to the Questions/Weaknesses.\n\n**Q1/W1. GPU memory and computational efficiency**\n\nWhen introducing our proposed insertion strategy of agg tokens, we mentioned that \"if agg tokens are added at the beginning,…, leading to significant GPU memory and computational overhead.\" This primarily refers to the advantages of our method during training. We highly agree with your suggestion. Here, we not only compared this method with inserting tokens at the beginning, but also used NetVLAD and SALAD as baselines. The results are shown in the table below. Our method has significant advantages over inserting tokens at the beginning in terms of GPU memory usage and training time, and also outperforms NetVLAD and SALAD.\n\n| Method | Training GPU Memory (GB) | Training Time/Epoch (min) |\n|----|---|---|\n| NetVLAD | 17.54 | 9.93 |\n| SALAD | 21.81 | 9.98 |\n| Insert at the beginning | 34.00 | 15.12 |\n| ImAge (Ours) | 16.73 | 9.87 |\n\nIt is also worth mentioning that we have demonstrated the advantages of our method in inference (inference time and descriptor dimensionality) in the paper (Figure 1 and Table 3). All these results show that our method outperforms previous methods in many aspects.\n\n**Q2/W2. transformer-based VPR methods with agg tokens**\n\nThank you for giving this suggestion to improve our paper. In fact, our method can be regarded as a general aggregation method for the transformer backbone. In addition to the experiments in the main paper, Appendix D (Table 7) also shows the results of combining the proposed ImAge method with ViT and CLIP. The R2Former you mentioned uses ViT as the backbone, so it is feasible for our method to improve the performance of its first stage (global retrieval). However, R2Former is a two-stage method with re-ranking using local features. Its training process is elaborate, and the model is trained on 8 Tesla-V100 GPUs, which makes it difficult to combine R2Former with our method during the limited Rebuttal period. Considering that R2Former is an excellent VPR method and ranked first in the leaderboard of MSLS Place Recognition Challenge when proposed (same as our method), and the re-ranking in it can significantly improve the VPR performance, we will discuss the potential of combining it with ImAge in our final paper and try to combine it in future experiments.\n\nTo show that our method can replace the aggregation method in other transformer-based VPR methods to achieve better performance, we replace the spatial pyramid GeM pooling feature in the CricaVPR work with our ImAge feature (denoted as CricaVPR+ImAge). The results are shown in the table below. It can be seen that our method significantly improves the performance.\n\n| Method | Tokyo24/7 | | MSLS-val | | Pitts30k | | Nordland |\n|----|---|---|---|---|---|---|---|\n| CricavPR | 93.0/97.5/98.1 | | 90.0/95.4/96.4 | | 94.9/97.3/98.2 | | 90.7/96.3/97.6 |\n| CricavPR+ImAge | 96.5/98.1/98.7 | | 92.0/97.2/97.3 | | 94.9/97.5/98.2 | | 94.1/97.9/98.7 |\n\nIn short, our ImAge is a general image representation method for the transformer-based VPR. In addition, when using the foundation model DINOv2 trained on a large-scale curated dataset, the performance improvement of our ImAge over other methods (such as NetVLAD) is more pronounced than when using ViT pre-trained on ImageNet. This suggests that in the future, as the performance of the foundation model improves (with more training data), the advantages of our ImAge may become more apparent.\n\n**W3. Novel for VPR**\n\nThanks for your recognition that our method is novel in VPR. We acknowledge that there have been some works in other fields previously on adding tokens to the transformer, as we discussed in Related Work. Our work provides a new perspective on the method of adding tokens: achieving implicit aggregation of patch tokens. As far as we know, this work is the first to do so not only in VPR but also in visual retrieval. Furthermore, our method achieves the SOTA performance in a concise way.\n\nBy the way, we are so happy to see that you appreciate our SOTA performance, real-world applicability, as well as the interesting attention visualizations of agg token (in Supplementary Material).\n\n**Thanks again for your valuable comments and suggestions. Please let us know if you have any further concerns. We'd be very glad to address them.**"}, {"review_id": "Qa5DV7wJZ3", "reviewer": "Reviewer_zpn8", "summary": "This paper proposes ImAge, a framework for visual place recognition that eliminates the need for explicit aggregators. Instead, it leverages the self-attention mechanism of a transformer backbone by inserting learnable aggregation tokens at a specific depth.", "questions": "Please see the above weaknesses.", "limitations": "The discussion of the limitations in this paper is insufficient and lacks transparency. The authors claim that “ImAge is broadly applicable to a wide range of image retrieval tasks,” but this assertion appears somewhat inverted in logic. In fact, prompt tuning methods have already been widely applied to mainstream vision tasks such as image retrieval, classification, and object detection. The proposed ImAge method essentially transfers the prompt tuning strategy to the relatively niche task of visual place recognition (VPR). However, the authors do not provide a systematic comparison between ImAge and existing prompt tuning approaches in other vision domains, limiting their analysis and validation to the VPR setting alone.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 1, "clarity": 3, "significance": 1, "originality": 1, "strengths_and_weaknesses": "Strengths：\n\nThe paper is well-written, with clearly articulated ideas that are easy to follow.\n\nWeaknesses：\n\n[1] The paper tends to rely heavily on storytelling, while the proposed method is essentially a form of prompt tuning. Compared to existing classical prompt tuning approaches (e.g., VPT [R1]), it lacks substantive innovation, offering only engineering-level adjustments to the number, position, and initialization of prompt tokens. Applying this method to visual place recognition (VPR), a classification task, is fairly intuitive. However, the authors only compare their method against other VPR-specific approaches, deliberately avoiding comparisons with established prompt tuning methods in the broader computer vision community.\n\n[2] The comparative experiments are not sufficiently rigorous. As shown in Table 6, the model's performance is highly sensitive to the number of aggregation tokens. Nevertheless, the ablation studies presented in Tables 4, 5, and 7 are only conducted under the setting where the number of agg tokens is 8. Therefore, the conclusions drawn from these ablations may lack generality and robustness.\n\n[3] The proposed initialization strategy for cluster centers is inspired by NetVLAD, but its rationale is not well justified. In fact, the mechanism for computing global representations in the two methods differs fundamentally: NetVLAD constructs global descriptors using residual statistics between patch features and cluster centers, whereas ImAge directly flattens the cluster centers to form the global representation. This conceptual gap is not clearly explained, which undermines the credibility of the design.\n\n[4] Moreover, the proposed method is highly sensitive to multiple hyperparameter choices, including the insertion strategy, initialization, and number of aggregation tokens. Due to the lack of theoretical justification, determining these key hyperparameters in new practical scenarios remains a challenge, which may significantly affect the model’s stability and generalization.\n\nReference：\nJia, Menglin, et al. \"Visual prompt tuning.\" European conference on computer vision. Cham: Springer Nature Switzerland, 2022.", "was_revised": true, "final_justification": "I indeed appreciate the authors’ further responses. However, it does not impress me substantively. I still insist that many papers have proposed similar CLS tokens and insert strategies in Transformer. An engineering modification for VPR task is insufficient to meet the acceptance standards of NeurIPS.", "rebuttal": "Thanks for your comments to help us improve the paper. The following are Responses to the Questions/Weaknesses.\n\n**W1. About the novelty of ImAge**\n\nWe sincerely appreciate your suggestion of conducting an experimental comparison between ImAge and prompt tuning (the results are in the table below). Regarding the novelty concerns, you might have some misunderstandings about our method. Before explaining the differences between our method and prompt tuning, we need to point out that VPR is an **image retrieval task**, not a classification task as you said. This might have led to some of your confusion about the sensitivity of our method (please see response to W2). To clarify the differences between our method and prompt tuning, we provide the following detailed explanations.\n\n-\tWe acknowledge VPT is an excellent parameter-efficient fine-tuning (PEFT) method applicable to many downstream tasks. However, it does not perform well in the VPR task as the prior work [1] demonstrated. The PEFT method, including prompt tuning, has been widely studied in the VPR task for as early as 1-2 years ago [2][1]. If our method is prompt tuning, similar performance would have already been achieved by prior work. However, the fact is that our method significantly outperforms previous methods and ranks first on the leaderboard of MSLS place recognition challenge.\n-\tIn terms of methodology, our approach differs from prompt tuning. Like several recent SOTA methods, our method adapts a pre-trained model to the VPR task by fine-tuning the last several blocks of the transformer backbone, rather than using prompts. It differs from prompt tuning where \"only the task-specific prompts are being updated during fine-tuning, while the transformer backbone is kept frozen\" [5]. The added agg token in our method is used to aggregate the key information in the patch tokens, rather than as the prompt.\n-\tIn fact, apart from prompt tuning, there are other works that add additional tokens to the pre-trained transformer for purposes other than fine-tuning the model. We have discussed this in detail in the second part of the Related Work. Our approach is more relevant to the other two types of methods than prompt tuning. We also discussed the differences between our approach and other methods in  Related Work, Section 3.2 (Lines 187-190), and Appendix C (especially in this part). In fact, our agg tokens are “output-oriented tokens” rather than “prompt tokens”. Meanwhile, our work is inspired by the use of memory/register tokens in DINOv2-register [3] to capture global information.\n-\tBesides, we also conduct fair comparative experiments to demonstrate that our method is different from prompt tuning (VPT). For applying VPT to the VPR task, an aggregation layer is necessary. We use the NetVLAD aggregation layer to obtain the final image descriptor. We trained two model versions of VPT, i.e., VPT (shallow) and VPT (deep). All models are based on the DINOv2-register backbone and trained on GSV-Cities, as in Table 3 of our paper. The results in the table below show that the performance of VPT is limited (basically consistent with [1]). The three methods in the last three rows (including our ImAge) fine-tune the DINOv2-register backbone by fine-tuning the last four encoder blocks (widely used by SOTA methods in VPR), achieving significantly better performance than prompt tuning (VPT). \n| Method | Pitts30k | MSLS-val | Tokyo24/7 | Nordland |\n|---|---|---|---|---|\n| VPT (shallow) + NetVLAD | 83.5/92.5/94.8 | 53.8/67.2/70.9 | 61.6/81.0/84.4 | 23.6/38.0/45.2   |\n| VPT (deep) + NetVLAD  | 90.8/95.7/97.0 | 84.7/93.1/95.0 | 92.4/97.8/98.1 | 52.5/67.8/74.1  |\n| NetVLAD | 92.8/96.6/97.8 | 91.8/96.5/96.6 | 95.6/98.1/98.7 | 90.5/96.5/97.8 |\n| SALAD | 92.5/96.6/97.5 | 92.6/96.6/97.0 | 95.6/97.5/99.0 | 86.5/93.6/95.7|\n| ImAge | 94.0/97.2/98.0 |93.0/97.0/97.2 | 96.2/98.1/98.4 | 93.2/97.6/98.6 |\n\n-\tIn short, the prompt tuning methods use the prompts for fine-tuning the pre-trained large model, rather than as the final representation for downstream tasks. In contrast, our ImAge directly uses the agg tokens as the final global representation, which presents an opposite design perspective. Besides, our proposed insertion and initialization strategies are not the ready-made method used in the prompt tuning research or simple engineering tweaks, but thoughtful designs to enable the added agg tokens to aggregate useful information from the patch tokens. Here, we also use the output tokens corresponding to the prompt tokens in VPT as final image descriptors, and the results are shown in the following table. There is still a significant gap compared to our method.\n| Method | Pitts30k | MSLS-val | Tokyo24/7 | Nordland |\n|---|---|---|---|---|\n| prompt tokens in VPT | 80.5/92.2/94.8 | 62.4/74.2/79.7 | 55.9/73.0/81.3 | 13.0/21.6/26.6 |\n| ImAge | 94.0/97.2/98.0 |93.0/97.0/97.2 | 96.2/98.1/98.4 | 93.2/97.6/98.6 |\n\n**W2. About the ablation studies**\n\nWe appreciate your concern about the robustness of our method. In standard ablation studies, the common practice is to fix all variables except the one being studied, in order to avoid the influence of other factors. In the prompt tuning research ([4], [5]) you mentioned, they were also fixing the prompt length when exploring the influence of other factors. As for your observation that \"the model's performance is highly sensitive to the number of aggregation tokens\" in Table 6, we would like to clarify that VPR is a retrieval task, and the performance of the method is directly influenced by the dimension of the image descriptors (the global feature vector). This holds true in all other VPR studies. The different numbers of tokens in our method directly form different dimensions of image descriptors, and this is reasonable in terms of its impact on performance. This is most pronounced on the Nordland dataset (very difficult), which is known to benefit from high-dimensional global descriptors (to provide detailed information). So, as you can see in Table 4, our method does not perform well using low-dimensional descriptors (i.e., only 1 agg token). In fact, this is also true for other methods. Compared with other low-dimensional descriptors, the results of our method on Nordland are actually better, e.g., the baseline (CLS token) in this table is 11.3% lower than our method on R@1. Moreover, on more commonly used benchmarks like Pitts30k and MSLS-val, our ImAge shows much lower sensitivity to the number of agg tokens. In addition, high descriptor dimensions will cause a significant storage burden and computational time consumption for retrieval tasks. Taking all aspects into consideration, we finally chose 8 agg tokens as the default setting. This results in a 6144-dimensional descriptor, which is superior to recent works (SALAD is 8448-dim and BoQ is 12288-dim).\n\n**W3. About the initialization strategy**\n\nSorry to confuse you. We appreciate your concern for the rationale behind our initialization strategy. We also agree that NetVLAD constructs global descriptors using residual statistics between patch features and cluster centers, which is different from that our ImAge directly concatenates agg tokens as global descriptors. However, we merely drew inspiration from the initialization of NetVLAD, and this initialization is not unique to NetVLAD. It is actually the widely used k-means clustering algorithm. Since these cluster centers represent common visual categories and provide more visual prior, we use the cluster centers to initialize our learnable agg tokens. Such initialization can provide guidance for agg tokens to learn meaningful global information better. This is also partially discussed in our method section and ablation studies section.\n\n**W4. Sensibility to hyperparameter choices**\n\nAs our response to W2, the performance of all methods in retrieval tasks varies with the descriptor dimensionality (corresponding to the number of agg tokens in our method). Regarding the insertion strategy and initialization method, it is precisely because other methods are not robust enough in the VPR task that we have proposed our insertion strategy and initialization method. **It should be noted that in Tables 4 and 5, only the best one is our method**. The other results correspond to other methods rather than different Settings of our method. This might be the reason for your misunderstanding. We have explained the reason for the specific design in the Method section. Ablation experiments demonstrate that our method is highly robust to various VPR scenarios, e.g., the Pitts30k (urban scene), MSLS (suburban and urban scene), and Nordland (natural scene) datasets. In addition, we are also happy to apply our method to more scenarios to verify its robustness.\n\n**Limitations**\n\nAs our response to W1, our method is different from prompt tuning. If you agree that our method is a new implicit aggregation approach to obtain global features in retrieval tasks, we believe you will also recognize that it is likely to be equally effective in other retrieval tasks. As far as we know, this implicit aggregation method that does not require an additional aggregator has not yet been used for other retrieval tasks.\n\n**Thanks again for your valuable comments and suggestions. Please let us know if you have any further concerns. We'd be very glad to address them.**\n\n**`References`**\n\n[1] Wu, Rouwan, et al. Investigating the Different Method of Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models on Visual Place Recognition. Available at SSRN 5233632.\n\n[2] Lu, Feng, et al. Towards seamless adaptation of pre-trained models for visual place recognition. ICLR 2024.\n\n[3] Darcet, Timothee, et al. Vision Transformers Need Registers. ICLR 2024.\n\n[4] Lester, Brian, et al. The Power of Scale for Parameter-Efficient Prompt Tuning. EMNLP 2021.\n\n[5] Jia, Menglin, et al. Visual prompt tuning. ECCV 2022."}, {"review_id": "YJ6RXzlhhW", "reviewer": "Reviewer_tSze", "summary": "The paper proposes a novel global descriptor for visual place recognition made of the concatenation of \"learnable tokens\" / registers stacked to the visual tokens of a visual-transformer-network.\nThe \"regular\" visual tokens are discarded.\n\nThe design of such global descriptor contrasts with existing architectures that follow a 2 step approach: extract visual features with a backbone and aggregate them with an aggregator network.\nOne issue that can arise then is any domain shift in the backbone feature extract hinders the performance of the aggregation network.\nDefining the global descriptor from the backbone network only addresses this issue.\n\nThe additional tokens can be stacked to the regular ones at different levels of the transformer network.\nThe paper motivates and demonstrates through experiment that the optimal results are obtained when the tokens are inserted after the last frozen layer.\nThe global descriptor is best when the learnable tokens are initialized based on the frozen layer outputs: clustering the frozen layer's feature space and using the centroid to initialize the learnable tokens leads to the best results.\n\nThe proposed global descriptor is evaluated following the visual place recognition guidelines (evaluation datasets, metrics, relevant and exhaustive baselines).\nThe results support the claim of the paper in that the proposed aggregation-free and register-based descriptors are powerful representations for visual place recognition and perform better or on than other transformer-based descriptors.", "questions": "- In Eq3, is there an interpretation to the term $Q_z K_a^T V_a$ that also reprensets interactions between the aggregation token / registers and the image embeddings?", "limitations": "Yes, the authors have adequately addressed the limitations and potential negative societal impact of their work (in the supp. material)\n\n### Misc. Comments\n\nUndefined terms:\n- L46: \"global optimiality\" of a global descriptor\n- L48: \"correction or refinement\" of a global desctiptor. Is this supposed to mean \"finetuned\"?\n\n\nUnjustified claims:\n- L50 \" With regard to specific methods like NetVLAD, there exist some problems, such as [...] the loss of position information of the original patch features.\". Why is that an issue? If this is related to re-ranking, there are works that enable re-ranking from the global descriptors only (e.g. \"Global Features are All You Need for Image Retrieval and Reranking\", ICCV23)\n\n- L354: \"These centers, obtained via unsupervised clustering of descriptors from randomly sampled training images, tend to capture common visual patterns (e.g., buildings, vegetation, vehicles, pedestrians, etc).\"\n   - how is it proved that these descriptors capture human level semantics?\n\nReferences:\n- L103: BoW's correct reference is not [4] but \n\nSivic, and Zisserman. \"Video Google: A text retrieval approach to object\nmatching in videos.\" Proceedings ninth IEEE international conference on\ncomputer vision. IEEE, 2003.\n\n- L30: [52] is a generic place recognition method so it is not a relevant citations related to the existing challenges in VPR", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "S1. Novel and interesting design of the global descriptor that demonstrate how visual transofrmers can learn global descriptors through register tokens.\nThe design is well motivated and the experiments demonstrate that it is suitable for place recognition.\n\nS2. Exhaustive experimental evaluation that support the claim of the paper.\n\nS3. Relevant related work.\n\nS4. Well written paper.\n\n\nW1. The paper claims to address the \"limitations of the previous backbone-plus-aggregator\" but the justifications are not sound so this claim does not hold.\nIt is best to simply state that the proposed descriptors is novel because it learns global tokens within the transformer architecture, which is a good contribution on its own.\n\nL81: \"This aggregator-free VPR paradigm addresses the limitations of the previous backbone-plus-aggregator paradigm by:\"\n- a) \"unifying feature extraction and aggregation toward global optimization, and\"\n    - Aggregation-based method enable global optimization by making the aggregation differentiable (e.g. NetVLAD, BoQ, ...) so this sentence is incorrect.\n- b) \"progressively implementing aggregation in cascaded transformer blocks, thus achieving the correction and refinement of image representations.\"\n    - \"correction and refinement of image representations\" is undefined. If this means improving the feature representations with each layers, all networks (CNN, VIT ...) do so\n\nL91: \"This new paradigm effectively and efficiently overcomes the limitations of the previous works\".\n  - Same comments as before: the design of previous works, i.e., aggregation-based descriptors, is not a limitation per se", "was_revised": true, "final_justification": "Thank you for the rebuttal.\nThe rebuttal has addressed all the concerns, mainly the confusion in some of the writing and to remove some unjustified claims.\nThe rating remains the same.", "rebuttal": "Thanks for your positive recommendation and detailed suggestions. The following are Responses to the Questions (Q), Weaknesses (W), and Misc. Comments (M).\n\n**Q. about the term $Q_z K_a^T V_a$**\n\nWe truly appreciate your attention to this detail. Indeed, this term also represents interactions between agg tokens and the image embeddings, but it is the influence of the former on the latter (unlike $Q_a K_z^T V_z$). Since we leverage the inherent self-attention mechanism within the backbone to jointly process all tokens, bidirectional interactions between agg tokens and image embeddings are unavoidable. When our agg tokens aggregate useful global information from the image embeddings, the image embeddings can also receive information from the agg tokens. This behavior is analogous to the interaction between the [CLS] token and patch tokens in the standard ViT architecture.\n\nWe did not explain this item in the method section to avoid confusing the readers. However, in our ablation studies, we explored the impact of setting different numbers of agg tokens. \nThe results show that agg tokens have no negative impact on image embeddings as long as the number of tokens is not too large. When the number of agg tokens becomes excessively large (e.g., 64), a noticeable decline is observed. This indicates that an excessive number of agg tokens can interfere with the contextual information of patch tokens, which is not recommended. It should be noted that an excessively large number of agg tokens will cause serious overhead in model inference, and the high-dimensional descriptors generated by these tokens will also cause serious overhead in retrieval. So, this setting does not meet the actual needs. That is, it is not necessary to view it as a negative impact. Several agg tokens have already been able to effectively aggregate the useful information of patch tokens.\n\n**W1. The limitations of the previous method**\n\nThank you for recognizing the novelty and contribution of our approach. We will follow your suggestion and directly state the novel perspective of our proposed method, removing some claims of the limitations of previous methods.\n\n-\tL81: a) “toward global optimization”. --> We agree with you that this sentence is incorrect. We should claim “unifying feature extraction and aggregation into a more cohesive framework”, rather than “toward global optimization”.\n\n-\tL81: b) “correction and refinement of image representations”. --> Sorry to confuse you. The image representations here refer to the global image feature (i.e., a representation vector). \nAlthough other methods improve the feature representations with each layer, what is improved is the patch descriptors (i.e., feature maps in CNN, patch tokens in ViT). The global image representation is formed after one-shot processing by the aggregation layer and is immediately output (without opportunity for correction and refinement). Our method, however, inserts agg tokens in the backbone's L-th encoder layer. These agg tokens serve as global image representations, and they are subsequently corrected and refined in subsequent encoder layers (synchronously with the refinement process of patch tokens), rather than being formed all at once. This is indeed different from the process of forming global representations in the backbone+aggregator approach. We will explain this in detail in the final paper to avoid confusion.\n\n-\tL91: ”… overcomes the limitations of the previous works”. --> We agree with you. We will delete “overcomes the limitations” and will revise this sentence as “Our method provides a novel perspective different from the previous paradigm, effectively and efficiently achieving better performance in the transformer era.”\n\n**M1. Undefined terms**\n\n-\t\"global optimality\".  --> Please see the response for W1 (L81 a)). We will delete this statement according to your suggestion.\n-\t\"correction or refinement\".  --> Please see the response for W1 (L81 b)). \"fine-tuned\" is also OK, but we are concerned that it might easily be confused with the training of models.\n\n**M2. Unjustified claims**\n\n-\t“The issue with NetVLAD” --> We acknowledge that some improvement efforts in the last two years have made the issue of the loss of position information less of a concern. However, the loss of position information objectively exists in NetVLAD/GeM, but our method does not have this issue. This is indeed a minor issue and is not sufficient to serve as the highlight/motivation of our approach. We will remove these statements from the main paper.\n-\t“How is it proved that these descriptors capture human level semantics?” --> Sorry to confuse you. Although we use unsupervised clustering of patch descriptors during initialization, the transformer backbone used to extract these patch descriptors is pre-trained on large-scale datasets, which can indeed provide high-level semantics [56]. One way to demonstrate this is to visualize the attention weights of agg tokens to patch tokens, observing which patches are most correlated with the agg token. The supplementary materials we submitted (a PDF in the zip file) show that agg tokens from the trained model only focus on objects that are helpful for VPR, which provide vivid and interesting qualitative results. Before training (after initialization), the agg tokens focus on not only buildings and vegetation, but also vehicles and pedestrians, i.e., common visual patterns. However, it should be noted that it does not mean that an agg token exactly corresponds to a human semantic object (e.g., the building or vegetation). From attention visualization, we can see that some tokens contain both buildings and vegetation, and there are also multiple tokens that focus on buildings. We will emphasize this point in the final paper to avoid misleading.\n\n**M3. References**\n\nWe sincerely apologize for citing secondary sources rather than the original work in the two instances you pointed out. For the first instance, we will cite the BoW work you mentioned. For the second instance, we will cite the work [47].\n\n**Thanks again for your valuable comments and suggestions. Please let us know if you have any further concerns. We'd be very glad to address them.**\n\n**`Reference`**\n\n[47] Stephanie Lowry, et al. Visual Place Recognition: A Survey. IEEE Transactions on Robotics, 2015.\n\n[56] Maxime Oquab, et al. DINOv2: Learning Robust Visual Features without Supervision. Transactions on Machine Learning Research Journal, 2024."}]

Author rebuttal:

Meta-review: This paper introduces ImAge, a new framework for Visual Place Recognition (VPR). Instead of adding a separate pooling or aggregation step on top of a Vision Transformer backbone, ImAge uses special "aggregation tokens". These tokens are inserted at a chosen layer within the transformer. They use the existing self-attention mechanism to combine information from the image. The output embeddings from these tokens directly serve as the global image descriptors for recognizing places.

The main contribution is an innovative implicit aggregation mechanism. This approach eliminates the requirement for explicit, standalone aggregators (like NetVLAD or GeM). By leveraging the transformer's native self-attention through aggregation tokens, it avoids critical issues found in traditional "backbone + aggregator" systems. These issues include sub-optimal separate optimization and potential loss of spatial information during aggregation. The proposed ImAge framework demonstrates superior performance over previous VPR methods.

The paper received one “Reject” rating, and three “Accept” ratings.

Reviewer WgNP thought the authors' rebuttal had addressed the issues with presentation and clarity, the authors validated their specific design and demonstrated strong results influential for future VPR research. Reviewer WgNP kept the "Accept".

Reviewer VZG8 thought the comments from the authors resolved the concerns about W1 and W2. Reviewer VZG8 finally gave the "Accept".

Reviewer zpn8, who gave a "Reject" rating, thought the authors' rebuttals did not impress him/her substantively. Reviewer zpn8 insisted that many papers had proposed similar CLS tokens and insertion strategies in Transformers. Reviewer zpn8 questioned the fundamental novelty of ImAge's aggregation tokens, viewing them as derivative engineering adjustments akin to prompt tuning (VPT).

Reviewer tSze thought the rebuttal had addressed all the concerns, mainly the confusion in some of the writing and to remove some unjustified claims. Reviewer tSze remained the "Accept".

Reviewers WgNP, VZG8, tSze all gave the “Accept” ratings. Although Reviewer zpn8 thought the novelty is not sufficient, the experiments show impressed results and the code will be publicly available. Therefore, the paper can be accepted.
Decision: Accept (poster)