ELViS: Efficient Visual Similarity from Local Descriptors that Generalizes Across Domains
Authors: Pavel Suma
Giorgos Kordopatis-Zilos
Yannis Kalantidis
Giorgos Tolias
Venue: ICLR 2026 Poster (2026)
Keywords: instance-level image retrieval
image re-ranking
local similarity
generalization
interpretability
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: a new model and an extensive evaluation benchmark for domain generalization of instance-level image retrieval re-ranking using local descriptors

Abstract: Large-scale instance-level training data is scarce, so models are typically trained on domain-specific datasets. Yet in real-world retrieval, they must handle diverse domains, making generalization to unseen data critical. We introduce ELViS, an image-to-image similarity model that generalizes effectively to unseen domains. Unlike conventional approaches, our model operates in similarity space rather than representation space, promoting cross-domain transfer. It leverages local descriptor correspondences, refines their similarities through an optimal transport step with data-dependent gains that suppress uninformative descriptors, and aggregates strong correspondences via a voting process into an image-level similarity. This design injects strong inductive biases, yielding a simple, efficient, and interpretable model. To assess generalization, we compile a benchmark of eight datasets spanning landmarks, artworks, products, and multi-domain collections,
and evaluate ELViS as a re-ranking method. Our experiments show that ELViS outperforms competing methods by a large margin in out-of-domain scenarios and on average, while requiring only a fraction of their computational cost.

Published as a conference paper at ICLR 2026
ELVIS: EFFICIENT VISUAL SIMILARITY FROM LOCAL DESCRIPTORS THAT GENERALIZES ACROSS DOMAINS
Pavel Suma'
Giorgos Kordopatis-Zilos'
Yannis Kalantidis ?
Giorgos Tolias'
IVRG, FEE, Czech Technical University in Prague NAVER LABS Europe
ABSTRACT
Large-scale instance-level training data is scarce, so models are typically trained on domain -peain- datasets. Yet in real-world retrieval, they must handle diverse domains, making generalization to unseen data critical. We introduce ELViS, an image-to-image similarity model that generalizes effectively to unseen domains. Unlike conventional upproaches, than representation space, promoting cross-domain transfer. It leverages local descriptor correspondences, refines their similarities through optimal transport step with data-dependent gains that suppress uninformative descriptors, and ag- gregates strong correspondences via voting process into image-level similar- ity. This design injects strong inductive biases, yielding simple, efficient, and interpretable model. To assess generalization, we compile benchmark of eight datasets spanning landmarks, artworks, products, and multi-domain collections. and evaluate ELViS as re-ranking method. Our experiments show that ELViS outperforms competing methods by large margin in out-of-domain scenarios and on average, while requiring only a fraction of their computational cost.
model operates in similarity space rather
Code available https//gitubb.co/aaadsssm////
INTRODUCTION
Chamfer=OT
1600
R Former
Instance-level image retrieval aims to iden- tify images of a specific object, whether product, within
performance (mAP)
800
AMBS
55
400
ELVIS
landmark, toy, painting, large image database. The best-performing ap- proaches rely on local descriptors (Cao et al., 2020; Tan et al., 2021; Lee et al., 2022; Zhu et al., 2023; Suma et al., 2024; Xiao al., 2025), incorporating an image-to-image simi- larity model to refine shortlist of the most sim- ilar images. This shortlist is initially retrieved using global image descriptors, often derived from foundation models (Oquab et al., 2024; Zhai al., 2023; Radford al., 2021).
200
50
100
10
10a
10a
time (ms)
Figure 1: Performance vs. time. Average perfor- mance across datasets and multiple domains for fixed numbers of re-ranked images indicated with text labels. All models are trained on the land- marks domain (GLDv2). Runtime is estimated from model latencies reported in T.
Generalization to unseen domains is essential for two reasons: (i) it is inherent retrieval, since training and test instances are disjoint, and (ii) collecting large instance-level training sets across diverse domains is notoriously chal- lenging. Nevertheless, most methods remain confined to single-domain evaluation. Models trained on landmarks or product pairs are typically tested on benchmarks from the same domain, leaving unclear to what extent they overfit and limiting their applicability in broader, real-world scenarios.
In this work, we challenge this paradigm by studying retrieval from a single-source domain gener- alization perspective, a setting mostly explored in classification (Csurka et al., 2022). We argue that in the era of foundation models, trained across diverse domains, using them off-the-shelf for both global and local descriptors a promising strategy for cross domain performance. Building on this, focus learning image-to-image similarity models for retrieval re-ranking that operate on sets

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Published as a conference paper at ICLR 2026
of local descriptors extracted from foundation models. This direction is supported by recent findings that a learnable similarity model (Suma et al., 2024) trained on frozen DINOv2 (Oquab et al., 2024) features generalizes well across domains (Kordopatis-Ziiee et al., 2025), despite being trained only on landmarks and not specifically designed for generalization.
We propose an Efficient Local Visual Similarity model, called ELViS, which operates on patterns of local descriptor similarity, i.e. correspondence patterns, rather than on descriptors of visual ap- pearance. This enables a more general and transferable image-level similarity measure, which is similar to observations in classical computer vision work (Shechtman & Irani, 2007). The local descriptor
reseriptorsiiilatt similarity matrix is refined using optimal transport (OT) with data-dependent gains that discard uninformative descriptors, followed by a learnable counting step that emphasizes strong correspondences. Notably, ELViS is conceptually simpler than existing methods, free of black-box modules, and built from sequence of intuitive and interpretable steps. The architecture carries a strong inductive bias; each design choice introduces explicit priors on how to infer global similarity from local similarities. It is substantially more efficient and generalizes significantly better (see Fig- ure 1) than prior approaches (Tan et al., 2021; Shao et al., 2023; Suma et al., 2024), which rely on heavy transformer architectures lacking priors and interpretability.
To evaluate instance-level image retrieval under domain generalization, we introduce benchmark- ing protocol that unifies eight existing datasets across diverse domains: landmarks (ROP+IM, GLDv2), household items (SOP), retail products (Product M, RP2K), artworks (MET), and multi- domain sets (ILIAS, INSTRE). Benchmarks are grouped into in-domain and out-of-domain test sets depending on the training domain. To our knowledge, this is the first work to conduct such an ex- tensive evaluation of single-source domain generalization in instance-level retrieval. Our evaluation models generalize better than descriptor-
confirms that similarity- overfit the training domain and excel only on seen distributions. With its learnable voting process and explicit mechanisms against overfitting, ELViS achieves even stronger generalization across unseen domains.
ones, which tend to
In summary, we introduce ELViS, novel similarity-based re-ranking model that a) operates di- rectly on sets of local-desrriptor similarities via a novel OT formulation, b) is composed of simple, lightweight components, and c) provides high degree of interpretability at multiple stages of the pipeline. We evaluate ELViS on eight diverse instance-level benchmarks and show that, in addition to being substantially faster, it delivers large performance gains out-of-domain datasets while matching the performance of much heavier models on the training domain.
2 RELATED WORK
Image retrieval re-ranking. Among re-ranking methods, one line of research focuses on query expansion (Arandjelović & Zisserman, 2012; Radenović et al., 2019; Shao et al., 2023; Gordo et al., 2020), primarily using global descriptors. Another approach, which is also the focus of this work, leverages local descriptors for improved re-ranking. In the Bag-of- words framework (Csurka al., 2004) with hand-crafted descriptors (Lowe, 2004), a common strategy is to impose simple geometric constraints (Sivic & Zisserman, 2003) or perform RANSAC-like verification (Philbin et al., 2007). Since then, these methods have been adapted to work with local descriptors derived from deep net- works (Noh al., 2017: Simeoni al., 2019), ultimately surpassing their hand-crafted predecessors.
Deep learning models have emerged as powerful alternatives to estimate image similarity based on local descriptor sets. State-of-the-art methods such RRT (Tan et al., 2021) and AMES (Suma et al., 2024) match local descriptors using standard transformer-based architectures. Unlike these models, which take descriptor vectors as input, an alternative approach is to compute local descriptor similarities first, forming a similarity-based representation of the image pair. Early similarity-based models are employed for video retrieval, computing Chamfer similarity at both the frame and video levels and employing 2D convolutional network to capture temporal relationships, and generaliz- ing across various video retrieval tasks (Kordopatis-Ziil
et al., 2019; 2023). For image retrieval, CVNet (Lee et al., 2022) densely computes similarities across all local descriptors and processes them with a computationally expensive 4D convolutional network. In contrast, R2 Former (Zhu et al., 2023) builds similarity representation from sparse sets of local descriptors and uses transformer architecture for similarity estimation. ELViS is also a similarity- -ased model, but it is significantly simpler, faster, and more intuitive, while promoting better generalization to unseen domains.
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In the context of fine-grained sketch retrieval, Chowdhury al. (2022) employ optimal transport to aggregate local region descriptors. In contrast to their formulation, which incorporates Lagrange multipliers for roos-modaa matching, adopt intropy-regulane tiable counting mechanism, particularly effective for cross-domain generalization.
OT and devise fully differen-
Domain generalization. Single-source domain generalization is predominantly investigated in im- age classification (Khosla al., 2012; Li et al., 2017; Csurka et al., 2022). The prevailing strategies involve synthetic data generation techniques that operate by augmenting training samples in the image space (Volpi et al., 2018; Xu et al., 202 bba) or representation space (Mancini et al., 2020; Zhou et al., 2021), by directly generating novel samples (Yue et al., 2019; Qiao et al., 2020). Lo- cal descriptors paired with BoW also show benefits for generalization classification (Wan et al., 2022). The generalization ability is obtained during training process that either starts from scratch or consists of fine-tuning a network pretrained on ImageNet.
In tasks such as image matching (Jin et al., 2020) and 3D reconstruction (Schönberger & Frahm, 2016), where open- open-world performance and generalization are essential, we observe a distinct trend compared to other computer vision tasks. methods (Schönberger & Frahm, 2016) remain among the cop-performing approaches.
representations (Lowe, 2004) and matching
major shift happens with the advent of large pre-trained foundation models (An et al., 2023; Zhai al., 2023; Oquab et al., 2024; Radford et al., 1). These models are exposed to vast amounts of data during training, making unclear whether a given test image truly belongs an unseen domain. Notably, keeping their representations frozen while applying hand-designed methods has proven highly effective across diverse object types and domains (Ornek al., 2024). While training a model on top of frozen representations may introduce domain dependence, carefully designed approaches have been shown encourage generalization (Jiang al., 2024).
3 METHOD
In this section, we introduce ELViS, an image-to-image similarity method that takes sets of local descriptors as input. Instead of operating directly on the descriptors, our approach builds, refines, and processes their similarity matrix, and enables learnable and intuitive voting mechanism with few parameters that generalizes well unseen domains. An overview is presented in 2.
3.1 BACKGROUND
Problem formulation. The goal of an image retrieval system is to search a database D using query image g and retrieve the most relevant images. At its core, image retrieval depends on a pairwise image-to-image similarity function s(g,z) E R, which measures the relevance between the query g and each database image x E 'D allowing for ranking based on similarity scores. We aim to learn by training on source domain, typically rich in instance-level training data, and then test on target domain that remains unseen during training.
Local descriptors. After an initial ranking with global descriptors, state-of-the-arr instance-level retrieval methods include a second-stage pairwise re-ranking step using local descriptors (Suma et al., 2024; Tan et al., 2021; Zhu et al., 2023). In ViT architectures (Dosovitskiy et al., 2021), these local descriptors correspond to a subset of the patch descriptors. Given an image æ, the set of local deseriptors represented
D' M matrix, X = [x1 x,.. xM]. where each descriptor X; D is D'-dimensional vector. We select the strongest M descriptors per image based on strength score (Suma et al., 2024). For efficiency and better task adaptation, the descriptor dimensionality is reduced from D' D through learnable linear projection. This projection is implemented as linear layer followed by layer normalization and lz fz-normalization per local descriptor. The projection is a common component among all learnabl methods we compare with in the experiments.
Image similarity. The similarity s(g, x) E R between images g and æ is computed as function of their corresponding local descriptor matrices Q X E D i.e., z)).z) == s(Q.X) The core of processes the local descriptor similarity matrix S Q x R Mr M For notational clarity, assume the same number of descriptors per image for and x, while the method is generic and does not impose such constraint.
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BCE Loss (training only)
estimate dustbin
optimal
gains
A(Q)
transport
f
BB
BCE
local descriptor dimilartty
Figure 2: Detailed overview of ELViS. The similarity matrix is refined using optimal transport with deseriptor-dependent dustbin gains. The strongest local similarities per descriptor are then selected and transformed clement-wiss by learned function f. before being sum-aggregated into scalar global similarity. During training, modified BCE loss with a learnable function g reshapes the penalty curve; g is used only for training and is expandable inference.
3.2 FROM LOCAL DESCRIPTORS TO LOCAL SIMILARITIES
The proposed approach operates the space of descriptor similarities, in particular similarity matrix S, whose values represent correspondences between the patches the descriptors are extracted from. We introduce refinement of S to generate matrix that emphasizes mutually consistent strong correspondences and discards correspondences from uninformative descriptors.
We formulate the problem as a variant of optimal transport which efficiently solved using the iterative Sinkhorn-Kang Knopp algorithm (Sinkhorn & Knopp, 1967) allowing back-propagation through the optimization process. More precisely, our objective is to find a matrix P, that maximizes (P, S) subject to constraints Pl 1M and P 1M M, where 1 is vector of ones of size M, and (s .) denotes the Frobenius inner product. The solution P is seen as a refined, doubly stochastic, similarity matrix. To allow distracting uninformative descriptors (e.g. those extracted from the background) to be ignored and excluded from the final correspondence matrix, we introduce slack variables that indicate the gain of not transporting mass for given descriptor. This is what SuperGlue" refers to as dustbins (Sarlin et al., 2020), i.e. the gain of assigning a descriptor to the dustbin and not to any descriptor in the other image. achieved by creating an augmented (M + 1) x (M + 1) matrix $ by
S v
$
(1)
where u, v E M contain the dustbin gains for the query and database image descriptors, respec- tively, while w accounts for the gain related to the total mass moved to the dustbins.
We define as the solution to the following optimization problem:
max P
(PS)) + AH(P)
Pl =a
1 b.
where a = [1 M] and b [1] M] are the marginal constraints extended to include dust- bins. We use retropy-r variant Sinkhorn- Sinkhorn-Knopp (Cuturi, 2013), with regularization term A. After optimization, we drop the additional dustbin row and column and maintain the refined similarity matrix P P.M.1:MM for the following steps.
Descriptor-dependent dustbin gains. Prior work (Sarlin et al., 2020) sets dustbin gains u, v to a fixed or learnalle scalar. Instead, predict the gain based on the descriptor itself with function h: TRR + R. The gains are given by
[1 Ui UM] U1 U.
=(h(q)) ... ((q))))))))))))()))))) h((m)] =(h(xi) ... ....xxxx...x//////
UM]
Note that we operate with similarity matrix and minimization. Similarity is seen as negative cost, or as the gain transporting mass.
matrix, therefore the maximization instead of
Prior work applies Sinkhorn-Knen Knopp similarity matrices establish point correspondences, while about the correspondence strengths and aim aggregate them into image-level similarity score.

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-10
-10-2
3
negudive pasttive Fio
0.4
g00)
density
2
density
4
0.5 50
0.2
1
0
0.01
0.02
50
100
local similarity s/g, sf
global similarity s(1, x)
Figure 3: Shape of the learned univariate functions f (left) and g (right). Although parame- terized as MLPs, both functions learn well-behaved scalar transformations that effectively separate matching and non-matching distributions. The distributions of input values visualized separately for positive and negative image pairs, sampled during training.
where u, and e denote the i-th element of vector and v, respectively. Larger dustbin gains of u and e assign higher chance for the correspondences of descriptor to be moved to the dustbin. We implement h two-layer MLP with GELU activation function (Hendrycks Gimpel, 2016). Gain w is learnable scalar. Our experiments demonstrate that using descriptor-dependent dustbin gains is essential for the effectiveness of such refinement step in the overall pipeline.
3.3 FROM LOCAL SIMILARITIES TO GLOBAL SIMILARITY
this step, we transform the similarity matrix S' into a set of votes that are aggregated into a single value representing the global similarity of the input image pair.
Strongest vote per descriptor: Given matrix S', which contains similarities for all pairs of descrip- tors, we keep the strongest similarity for each descriptor of each of the two images, acting as a vote. This is equivalent to selecting the strongest correspondence per descriptor. Formally, this is given by
5/
max jE (l.....
Sk
sf
max iE(L....M) S
Vi,j E (L...., M}.
where sf and s/ are row- and column- max-pooled similarities Summing all similarities in s and s'y jointly, for i.j = 1 M. is equivalent to computing Chamfer similarity on S' under the assumption of equal descriptor set cardinalities. We go one step further in the next processing stage. It is worth noting that Chamfer similarity after vanilla optimal transport, even without learn- ing, already serves as a strong baseline for generalization, as confirmed by our experiments, which motivates our choice to build on and extend this architecture.
Learnable vote strength and counting. We transform votes s/ and s via function f R TR real function mapping input scalar similarity Function f is implemented by two-layer MLP with GELU activations and sigmoid at its output. The image-to-image similarity then computed by counting all votes via summation
updated scalar similarity, i.e. vote, in [0, 1].
M
M
s sqg. x) = Σ
+
f(()
(5)
This voting-based global similarity estimation is inspired by classical works in image retrieval (To- lias & Jégou, 2014), which demonstrate that the number of strong local descriptor correspondences
We equivalently define s; and sy from max pooling in S, which is only used for visualization purposes.
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Figure 4: Visualization of the 25 strongest correspondences (votes) among 8;, 8j (left) and 8/; (right) before (left) and after (right) refinement with optimal transport. Red (yellow) represents high (low) similarity. Raw similarity values in (left) and values S' after passing them through f (right) used. Heatmaps represent the dustbins values by evaluating h densely for all patches in both images; bright values indicate large dustbin gain and uninformative descriptors.
robust indicator of image similarity. In contrast to hand-crafted weighting functions for corre- spondence strengths, like RBF-kernel (Jégou et al., 2008) or monomial kernel (Tolias et al., 2013), our learnable function f adaptively transforms the similarities to optimize retrieval performance. We visualize the learned f after training in Figure 3, which noticeably differentiates from linear weight- ing, i.e. identity function is ʃ and the corresponding MLP would not be included the model. Our experiments show that using learnable f is beneficial for generalization; excluding it makes the descriptor projection layer responsible for obtaining appropriate correspondence strength and the method more descriptor-dependent and domain-dependent.
Example visualization. Figure 4 shows the strongest correspondences selected from the similarity matrices before and after refinement, i.e. from S and S', respectively. Without refinement, many strong correspondences are formed between background or non distinctive regions; the refinement step suppresses these mainly due to the use of dustbin gains. The final set contains large number of correct object correspondences, whose strengths are meaningfully transformed by f. Summing these strengths yields the final similarity score between the two images, making the process both intuitive and interpretable.
3.4 TRAINING AND INFERENCE
Training. We introduce a data-adapted variant of the Binary Cross-Entropy loss (BCE) to train ELViS with positive and negative image pairs. Standard BCE minimizes log p for positive pairs and - log(1-p) for negative pairs, where = s(q, x) is the predicted similarity. In our formulation, is first passed through a learnable function g R - [0. before BCE is applied, yielding losses - (p) and - log(1 - g(p)). This modification no longer optimizes the log-likelihood of the predicted probability but instead the log of transformed version of it. By reshaping the penalty curve through we control which prediction errors are emphasized or downweighted. The function g is implemented as two-layer MLP with a sigmoid output, and its learned shape shown in Fig- ure 3. Empirically, tends to be nearly piecewise piceewis-llinear, with its slope changing around the region where positive and negative pairs start overlap more, thereby enabling differentiated penalization of emrrs. Thus, training proceeds under warped notion of similarity defined by g-
Inference and re-ranking with ELVIS. At inference, the auxiliary function is discarded. This strategy parallels the use of projection heads in self-supervised learning (Chen et al., 2020; Zbontar al., 2021) that are expendable modules used for optimization, encouraging generalization to other tasks. Discarding g is valid because, similar to a learnalle temperature in contrastive losses (Radford al., 2021), scales the similarity for the loss without altering the ranking order, provided it is an

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Published as a conference paper at ICLR 2026
increasing function. Although monotonicity is not enforced during training, consistently observe g to be monotonic, where matters, in practice. Attempts to enforce monotonicity explicitly, e.g. by constraining MLP weights to be non-negative in (You et al., 2017), slightly degrade performance and require further exploration.
Given ranked list of candidate images for query, obtained for instance using global- representation- based retrieval method, we form query-candidate pairs and apply ELViS to compute refined similarity scores, which are then used to re-rank the list.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate the proposed method and the most related approaches on 8 datasets con- taining instance-level annotations and spanning multiple domains: (i) Landmarks ROxford and RParis, reported jointly as ROP+IM (Philbin et al., 2007: 2008; Radenović et al., 2018), and GLDv2 (Weyand et al., 2020); (ii) Household items SOP (Song et al., 2016); (ii) Retail products ProductIM (Zhan et al., 2021) and RP2K (Peng et al., 2020); (iv) Artworks MET (Ypsilantis et al., 2021), )); and (v) Multi-domain ILIAS (Kordopatis-Zilon Jiang, 2015). Further details are found in the Appendix A.
al., 2025) and INSTRE (Wang &
Training domains. We go beyond the standard evaluation commonly adopted in instance-level trieval papers, i.e. training on GLDv2 due to its large number of images and instances and evaluating on the same domain, e-g. GLDv2 and ROP+1M. We put focus generalization and introduce a protocol consisting 8 instance-level retrieval datasets spanning diverse visual domains. De- pending on which dataset is used for training the re-ranking models, we categorize the datasets into two groups: in-domain and out-of-domain, and report the average performance separately for each. We select two large datasets with clearly defined train/test splits as training domains: GLDv2 and SOP. When training on GLDv2, we consider its test set, and ROP+IM, as in-domain testing and the remaining 6 datasets as out-of-domain. When training on SOP, we consider only its test set as in-domain testing, while the remaining 7 datasets are treated as out-of-domain.
Evaluation protocol. Retrieval performance is measured using mean Average Precision (mAP) on ROP+IM and mAP@K K on the rest of the datasets. We additionally report average performance over all datasets in each group, i.e., in-domain and out-of-domain. We re-rank the top 400 retrieved images in all experiments. As in AMES, we select the M = 600 strongest descriptors according to local feature detector (Noh et al., 2017; Tolias al., 2020) trained with images from the corre- sponding domain. We extract local descriptors with DINOv2 (Oquab et al., 2024), as in AMES, as well as DINOv3 (Siméoni et al., 2025) and SigLIP2 (Tschannen et al., 2025).
Compared methods. We compare the performance of the proposed ELViS with the most rele- vant re-ranking approaches from the literature, namely Reranking Transformer (RRT) (Tan et al., 2021), Forrrmer (Zhu et al., 2023), and AMES (Suma et al., 2024). We also compare to hand- crafted Chamfer similarity (Barrow et al., 1977; Razavian et al., 2016) applied directly on S, and on the refined matrix S' obtained via vanilla optimal transport (OT) with fixed dustbin gains equal to 1. Both these methods serve baselines for local descriptor performance without training re-ranking model. ELViS internally performs a similar process to matching correspondences, thus also compare to established feature matching models in the Supplementary material (Section D). Retrieval using only global representation is also evaluated, i.e., there is no use of re-ranking. We train and evaluate all methods using the publicly available AMES repository". and integrate the official implementations provided by the authors of RRTS, and FoFomrer".
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Method
ROP+1M GLDv2 ILIAS
INSTRE MET ProdIM RP2K SOP-1k
ID
OOD
avg
landmarks domain (GI.Dv2)
No re-ranking
57.7
27.3
9.4
65.3
61.6
24.7
39.0
33.7
42.5
38,9
39.8
Chamfer'
56.7
23.8
6.2
37.3
17.8
48.6
40.2
36.8
37.6
Chamfer+OT
60.6
23.8
14.3
76.0
74.0
35.1
55.4
46.2
42.2
50.2
48.2
RRT
69.2
33.1
13.1
72.4
64.1
29.3
60.7
52.1
51.1
48.6
49.2
RC Former!
68.5
32.6
15.2
77.6
72.0
47.7
43.7
48.6
49.1
AMES
70.1
34.7
14.6
75.6
70.7
32.3
56.5
48.5
52.4
49.7
50.4
ELVIS'
68.8
32.2
18.8
41.5
50.5 (-19)
55.0 (443)
53,9
houschold items domain (SOP)
No re-ranking
57.7
27.3
9.4
65.3
61.6
24.7
39.0
33.7
33.7
40.7
39.8
Chamfer'
46.2
15.4
6.7
63.2
45.2
24.7
50.3
50.2
50.2
35.9
37.7
Chamfer+OT'
52.3
18.6
11.7
75.9
71.6
37.5
52.7
45.8
45.8
45.7
45.7
RRT
43.4
10.8
12.2
68.4
25.5
32.2
46.9
57.1
57.1
34.2
37.1
R' Former!
55.5
23.7
12.9
73.4
59.3
32.8
42.0
51.1
51.1
42.8
43.8
AMES
55.6
17.2
12.4
72.2
44.2
36.7
51.8
56.7
56.7
41.4
43.3
ELVIS
59.7
22.9
18.6
81.1
44.1
54.9
51.0
51.5
Table 1: Domain generalization performance (mAP). Training performed either on landmarks
(GLDv2) or household items (SOP). Results reported per dataset and as average over in-domain (ID), out-of-domain (OOD), and all datasets (avg). Local descriptors extracted with DINOv2 (Oquab et al., 2024). Gray indicates in-domain results. Green (Red) highlights gain (loss) of ELViS over the second best method. i. indicate similarity-based and descriptor-basee models, respectively.
Method
ID
OOD
avg
Method
ID
0OD
avg
No re-ranking
49.6
60.5
57.8
No re-ranking
25.4
57.8
49.7
Chamfer
42.3
44.5
44.0
Chamfer
32.8
58.7
52.2
Chamfer+OT
41.7
51.4
49.0
Chamfer+OT
29.3
62.3
54.1
RRT
57.1
56.2
56.4
RRT
37.5
58.4
53.2
Former
56.0
63.9
61.9
R Former
35.2
63.0
56.0
AMES
58.6
62.8
61.8
AMES
37.1
62.7
56.3
ELVIS
56.9
67.4
64.8
ELVIS
36.4:41m
68.7 (45.7)
60.6
(a) DINOv3 (Siméoni et al., 2025)
(b) SigLIP2 (Tschannen et al., 2025)
Table 2: Performance (mAP) comparison using local descriptors from additional foundational models. Training performed on landmarks (GLDv2). Results reported per dataset and as averages over in-domain (ID), out-of-domain (OOD), and all datasets (avg). Green (Red) highlights gain (loss) of ELViS over the second best method.
4.2 RESULTS
Performance comparison. We present performance comparison using DINOv2 and two different training sets in Table and DINOv3 and SigLIP2 and training on landmarks in Table 2. We maintain backbone consistency between local and global similarity. i.e. the same model is used for initial global retrieval and re-ranking with local descriptors. We identify the following key observations:
(i) ELVS achieves the best average performance overall. Across all settings, ELViS outperforms all other methods in terms mean average precision by a significant margin, ranging from 2.9 5.8, compared to the second best approach.
(ii) ELVIS excels at domain generalization. The performance gains for OOD datasets are large, f.e. improvements over the second best method equal to 4.8 and 5.3 while training landmarks and household items, respectively, using DINOv2, and 3.5 and 5.7 using DINOv3 and SigLIP2, respectively, while training landmarks.
(iii) ELVIS provides significant gains on harder datasets. This is particularly evident in the case of the recent ILIAS datasets, which feature database of over 100M images. Here, the relative perfor- mance improvement of ELViS over the second-bes best method is over 23% and 36% while training on landmarks and household items, respectively.
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Method
ID
0OD
avg
No re-ranking
42.5
38.9
39.8
ELViS
50.5
55.0
53,9
w/o
dustbin
23.1 (-27.4)
32.8 (-22.2)
30.4 (-23.5)
w/o
deseriptor-dependent gain
48.8 (-1.7)
52.4 (-2.6)
51.5 (-2.4)
w/o f function
50.8 (+0.3)
53.8 (-1.2)
53.1 (-0.8)
w/o function
45.6 (-4.)
49.5 (-5.5)
48.5 (:5.4)
w/o f.g functions
47.3 (.3.2)
48.5 (.6.5)
48.2 (-5.7)
w/o descriptor projection
48.4 (.2.1)
51.7 (3.3)
50.8 (331)
Table 3: Ablation study method components. Average, ID, and OOD performance, when
training on landmarks. Green (Red) highlights gain (loss) over ELViS.
(iv) Similarity- models are more robust in OOD but weaker in ID. This trend extends beyond ELViS; all similarity-band based models seem to be top performing in OOD, but lag slightly behind in ID. For example, ELViS performs about 1-2 mAP worse than AMES on ID. Transformer models oper- ating local descriptors effectively overfit to the training domain, while similarity-base models generalize due to strong inductive biases.
(v) Hand-crafted similarity on top of strong foundational model representations' is strong baseline for OOD, Chamfer+OT is the second best performing method across 3 out 4 settings in OOD, This supports choice of extending such architecture with minimal learnable parts that significantly boost performance without compromising speed (Figure 1) or interpretability. Note that Chamfer by itself is not an effective re-ranking strategy, indicating the value of OT and similarity refinement.
Ablations. In Table 3, we present an ablation study of the proposed approach, analyzing the con- tribution of its internal components. Naively applying OT without dustbins (trained/tested only for an equal number of descriptors for both images) leads to a severe performance drop because unin- formative descriptors are not ignored. Learning scalar gain for all descriptors, in Sarlin et al. (2020), degrades performance and demonstrates the value of our contribution. Interestingly, when removing function f, with or without the presence of g, the model relies directly input descriptors to form vote strengths, therefore encouraging overfitting to the training domain, which improves ID performance at the expense of OOD generalization. Function is essential for effective training of ELViS, and its introduction results in a noticeable boost. Lastly, we additionally evaluate the impact of the descriptor projection the earlier learnable layer, which gives boost both ID and OOD,
Complexity analysis. Table 5 presents the computational complexity of ELViS compared to the best competitors. ELViS is the most lightweight and fastest model, containing the fewest network parameters, i.e. about 2x fewer than Former and about 20x fewer than AMES and RRT. Impor- tantly, ELViS is several times faster. As further illustrated in Figure 1, this efficiency enables ELViS to re-rank significantly more images, yielding an additional performance boost over other methods we consider fixed time budget. Notably, compared to the Chamfer+OT baseline, ELViS is as fast, while its newly added learnable components, i.e. data-dependent dustbin gains, functions f and g, and the descriptor projection, give strong performance boost. Note that when measuring latency, the projected local descriptors and dustbin gains for database images are considered precomputed and stored. Obtaining these components for the query image is constant cost that does not depend on the number of images to re-rank, and is therefore excluded from the reported times.
Improved in-domain performance with hybrid architecture. ELViS excels in unseen domains, yet is weaker than the SotA descriptor- based approaches in the seen domain. To reduce this gap, we design a hybrid model combining AMES and ELViS. We replace the standard descriptor projection used ELViS with sequence AMES transformer blocks. The two input descriptor sets are passed through this AMES module, comprising several self- and cross-attention layers. The resulting transformer outputs are treated as refined descriptor sets for each image, which are then passed to ELViS. We train this hybrid model end-to-end using the default ELViS parameters. Table shows the combination significantly boosts ID performance, with only a slight compromise in OOD.

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Method
Params (K)
Latency (ju)
Method
ID
OOD
avg
Chamfer+OT
0
No re-ranking
42.5
38.9
39.8
RRT
2232
656
AMES
52.4
49.7
50.4
ELViS
50.5
55.0
53.9
R FoFoer
202
782
AMES
2130
952
ELVIS AMES
52.1
54.7
54.0
ELVIS
96
101
Hybrid architecture combining descriptor-base based and similarity-based pro- cessing. In the hybrid model, AMES performs intra-image and inter-image descriptor process- ing with 5 transformer blocks, then the refined output tokens are subsequently fed into ELViS.
Table 4:
Table 5: Computational complexity. Param- eters include all learnable components and de- scriptor projection. Latency measures the av- erage similarity estimation time per image pair (batch size 500), excluding constant costs for query and precomputed database extraction.
OOD
avg
performance (mAP)
50
50
50
40
AMES
40
40
ELVS
0 0.2
5 10
100
0 0.2
5 10
100
0 0.2
10
100
training set (% of full)
training set (% of full)
training set (% of full)
Figure 5: Effectiveness with small training sets. Performance of ELViS and AMES when trained on subsets of the full training set (754k images), ranging from 0.2% to 100%. No re-ranking per- formance indicated at 0 training set size. Training hyperparameters tuned for each subset size based on validation performance.
Training with smaller training sets. To assess the data efficiency of ELViS, simulate limited- data scenarios by training on random subsets of our full training set from GLDv2. We evaluate models trained on 1.4k (50), 8.1k (250), 40k (1250), and 80k (2500) images (classes), correspond- ing to roughly 0.2%, 1% 1%, 5%, and 10% of the default training set. As shown in Figure 5, ELViS demonstrates strong generalization even low-data regimes. With only 1.4k images, ELViS al- ready outperforms the global baseline with a substantial margin. Furthermore, we observe distinct scaling behaviors for and OOD, i.e. while ID performance saturates relatively early. OOD per- formance continues to improve. This suggests that smaller datasets are sufficient for in-domain retrieval, whereas learning robust, transferable similarity patterns requires larger-scale training. Fi- nally, AMES starts improve global descriptor performance only with at least 80k images, high- lighting the data efficiency of ELViS.
CONCLUSION
We introduce ELViS, lightweight and highly effective image-to-image similarity model that achieves state-of-the-art re-ranking performance across multiple instance-level retrieval bench- marks. As a similarity-based model, ELViS benefits from strong inductive biases, enabling better generalization to unseen domains compared to descriptor-based approaches. Moreover, ELViS is composed of a sequence of intuitive processing steps. By avoiding deep stacks generic neural blocks, the model offers both improved interpretability and exceptional efficiency. In fact, it pro- cesses nearly an order of magnitude more images than the second-best model across all datasets in the same amount of time.
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Acknowledgements. This work was supported by the Junior Star GACR (grant GM 21-28830M), CTU in Prague (grant no. 36S231133/HH3/31/133, Horizon MSCA-PF (grant no. 101154126), and the Czech National Recovery Plan-CEDMO CEDMO 2.0 NPO (MPO 60273/24/21 3002// 1000) provided by the Ministry of Industry and Trade. We acknowledge VSB Technical University of Ostrava, ITAInnovations National Supercomputing Center, Czech Republic, for awarding this project (OPEN-35-4) access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the LUMI consortium through the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (grant ID: 90254),
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Appendix
validation set
test set
evaluation metrie
dataset
train set
domain
queries
database
queries
database
GLDv2
754K
379
762K
750
762K
landmark
mAP@ 100
SOP
48.9K
1K
10.6K
1K
60.5K
houschold
mAP@ 100
ROxford
70
5K+1M
landmark
mAP
RParis
6.3K+1M
landmark
mAp
ProductIM
6.2K
38.7K
retail
mAP@@ 100
RP2K
10.9K
10.9K
retail
mAP @ 100
MET
1K
397K
artwork
mAP @ 100
INSTRE
1.2K
27K
multi
mAP @ 100
ILIAS
100M
multi
mAP @ IK
Table F: Dataset statistics and metrics used. For each dataset, we use the most commonly used metric. Train and validation set statistics are reported only for the two training datasets that are used in this work.
A DATASET DETAILS
We evaluate the proposed method and the closest related approaches on eight datasets containing instance-level fine-grained recognition annotations. These datasets span multiple domains listed below. Examples of each are visualized in Figure
Landmarks. The ROxford (Philbin et al., 2007; Radenović et al., 2018), RParis (Philbin et al., 2008; Radenović et al., 2018), and Google Landmarks Dataset v2 (GLDv2) (Weyand et al., 2020) designed for instance-level retrieval and recognition. For training, the training split of GLDv2 as in AMES (Suma et al., 2024). As usual, we evaluate the medium and hard settings of the ROxford and Parii datasets together with 1M accompanying distractor images, denoted as ROP+IM.
the same subset of
Household items. Stanford Online Products (SOP) (Song et al., 2016) is an instance-leve dataset of furniture and electric appliance images sourced from eBay. It has been widely used for fine-graing gra-grai image classification and contains a training/test split. For evaluation on SOP, we sample 1 test images that serve as queries. The entire test set is used for the database. The training images are further divided into training set and validation set in an 80%-20% split.
Retail products. ProductIM (Zhan et al., 2021) and RP2K (Peng et al., 2020) are datasets contain- ing a large variety of retail products, e.g. cosmetics and grocery store items. The former was made for instance-level retrieval, while RP2K originally targeted fine-grained image classification. We adopt its repurposed version from (Ypsilantis et al., 2023), tailored for retrieval.
Artworks. The MET (Ypsilantis et al., 2021) dataset depicts artworks from the Metropolitan Mu- seum of Art New York and is designed for instance-level recognition. To adapt the benchmark for retrieval, we keep only one positive image per query that is guaranteed to have visual overlap with
Multi-domain datasets. Instance-Leva Image retrieval At Scale (ILIAS) (Kordopatis-Zibl et al., 2025) and INSTance-level visual object REtrieval and REcognition (INSTRE) (Wang & Jiang, 2015) datasets are designed for instance-level retrieval and include images from various domains, e.g- landmarks, products, and art.
B IMPLEMENTATION DETAILS
We follow the standard practice of training image-to-imagee similarity models with local descrip- tors (Tan et al., 2021; Suma et al., 2024). All the learnable parameters of ELViS are trained with binary cross-entropy loss, where the ground truth label of the image pair denotes whether the two
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(a) ROxford
(b) R.Parris
(c) GLDv2
(d) SOP-1k
(c) Product IM
(f) RP2K
4g) MET
(h) INSTRE
(i) ILLAS
Figure F: Benchmark examples. Three random samples are shown for each of the datasets. All images are resized to a square aspect ratio for better visualization.
images are positive, i.e. depict the same object instance, or not. We apply balanced sampling so the network sees the same number of both labels per batch. We mine challenging pairs by exploiting the most similar images as indicated by a given global descriptor representation.
Our model consists of function f implemented an MIP with hidden dimensionality of 16, and a function g. also an MIP, with a dimensionality set to 64. For the refinement step, we set 0.1 and initialize w with 1. We run 10 iterations of the Sinkhorn-Knopp algorithm, which is a trade-off that balances the model speed and performance. For the dustbin function h, we retain the input dimensionality D for the hidden layer. Local descriptors are extracted from the DINOv2- Base model with registers (Oquab et al., 2024; Darcet et al., 2024), DINOv3-Larg model (Siméoni 512 (Tschannen et al., 2025). The same models also provide the global descriptor to generate the respective ranked lists of images per query for re-ranking. The dimensionality after reduction is set to D 128.
al., 2025), and SigLIP2-So400m
We train our model for 10 epochs, sampling triplets of anchors, positives, and negatives as de- scribed in (Suma et al., 2024). We use a batch size of 200 triplets and a variable number of lo- cal descriptors per image, sampled within the range [100, 400). The network is trained using the Adam W (Loshchilov & Hutter, 2019) optimizer with learning rate ing rate schedule with warmup. We follow the same training strategy for both GLDv2 and SOP, with the exception of the learning rate and number of epochs on the SOP dataset, which are set to 1 10-3 and 30 for ELViS, and 5 10-1 and 45 for the compared models, respectively.
10-4 and a cosine learn-
For local descriptor extraction, images are resized according to their longer side. We use 770 for DINOv2 and 768 for DINOv3 and SigLIP2 ensure divisibility by the ViT patch size. The patch tokens output by the backbones are then processed by a local feature detector, which assigns an importance weight each token. Tokens with the highest weights are selected as local descriptors. Following AMES, we adopt its training strategy and network architecture for the local feature de- tector, training a different model for each backbone and training set. The dimensionality D' of the output local descriptors is 768 for DINOv2, 1024 for DINOv3, and 1152 for SigL.P2.
It common practice to ensemble retrieval similarities from global and local descriptors (Zhu al., 2023; Suma et al., 2024). However, we found this was not necessary for our method to achieve its best performance. In all our experimental results, we report the following for each method: global+local for AMES, R2Former, and local-only for RRT, Chamfer, and ELViS. The ensembling parameter for AMES is tuned on the in-domain validation set, while for R2Former, it is fixed to an equal weight (0.5) for local and global similarity. We do not enforce consistent setup across all methods, as no single ensembling scheme yields the best results in every setting; instead, we use the ensembling strategy proposed by the original works.
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Method
Alps
California
Gobi Devert
Amazon
Toshka Lakes
R@1
R@
100
RŒ1
Rē 10
R
R@!
R@10
RØ 100
10
R 100
R@1 R@ 10
R
AnyLoc
40.7
70.8
92.0
48.7
75.0
91.6
28.7
57.0
81.7
38.6
63.8
86.2
63.7
84.5
96.3
AstroLoc
98.1
99.5
99.8
97.4
99.2
94.6
99.2
99.9
93.0
96.9
99.1
99.0
99.6
99.9
No re-ranking
35.7
65.6
91.1
50.1
78.2
93.9
26.2
53.3
84.0
31,4
61.1
83.9
52.4
78.1
94.9
AMES
44.0
74.5
93.7
57.4
83.0
98.7
36.0
64.7
87.0
40.4
69.2
87.8
61.5
85.4
96.8
55.1
82.7
49.7
86.9
54.4
76.2
90.9
Table G: Generalization to extreme domain shifts. Evaluation on retrieval for Astronaut Photog- raphy Localization (APL) (Berton et al., 2024). Performance is measured in Recall@ k (R@ (). APL covers different geographical areas of extreme visual environments. Top: reference methods Any- Loc (Keetha al., 2023), a universal Visual Place Recognition (VPR) approach and the specialist AstroLoc (Berton et al., 2025) trained for the task. Bottom: Re-ranking 400 images on top of DINOv3 backbone. Bold indicates the best performance.
C GENERALIZATION TO EXTREME DOMAIN SHIFTS
We evaluate robustness five test sets of the Astronaut Photography Localization (APL) bench- mark (Berton et al., 2024). Although the downstream application is localization, the benchmark is formulated as standard image retrieval task evaluated via Recall k. The query images are hand- held photographs taken by astronauts, while the database consists of nadir satellite imagery. The goal is to retrieve, for each astronaut photograph query, the corresponding satellite image depicting the same location within the top k ranks. The benchmark includes datasets representing extreme vi- sual environments with distinct scientific importance, such as flood monitoring or disaster response. Table G compares the performance of ELViS and AMES, both using the DINOv3 descriptors and re-ranking 400 images. For reference, we also report performance of two task-specific approaches. ELViS demonstrates high effectiveness compared with the global baseline and AMES.
D COMPARISON WITH IMAGE MATCHING METHODS
We compare ELViS against established image matching methods. Specifically, we evaluate Super- Glue (Sarlin et al., 2020) and OmniGlue (Jiang et al., 2024), alongside a spatial verification ap- proach (Philbin al., 2007). To adapt these matching methods for similarity estimation, we use the count of correspondences and inliers, respectively, as the re-ranking score. This score is ensembled with the global retrieval score via a combination parameter, tuned as in AMES. We consider the landmark datasets as in-domain (ID) for these methods; both SuperGilue and OmniGiue are trained with strong correspondence supervision on landmark datasets, including MegaDepth and the IM distractor images of ROP+IM.
We evaluate SuperGlue" (pre-trained outdoor model) and OmniGlue" (SuperPoint DINOv2 back- bone) from their official repositories with the default parameters. To maintain consistency with our pipeline, the number of SuperPoint keypoints is capped at 600. Input images are resized such that the longer side is 1024 pixels, ensuring typically-sized images produce sufficient key- points while keeping resolutions comparable. For spatial verification, homography is estimated via MAGSAC++ (Barath al., 2020). Tentative correspondences are obtained by computing optimal transport with dustbins on the descriptor similarity matrix, thresholding these refined similarities, and applying a correspondence reciprocity filtering stage. The similarity threshold, inlier threshold, and reprojection error hyperparameters are tuned on the GLDv2 validation set.
As shown in Table H. SuperGlue is a strong baseline for OOD generalization. performs compet- itively with approaches specifically trained for image-to-ing -mage similarity (see Table 1). OmniGlue performs poorly, but still improves compared to no re-ranking. Note that both are originally de- signed for a different task and not for estimating the image-to-ingg to-image similarity via correspondence counting, as we do in this comparison. Spatial verification improves the initial ranking but remains inferior to Chamfer similarity as the alternative parameter-free option.
17

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Published as a conference paper at ICLR 2026
Method
ROP+IM GLDv2
ILIAS
INSTRE
MET
ProdIM
RP2K
SOP-1k
ID
OOD
No re-ranking
57.7
27.3
9.4
65.3
61.6
24.7
39.0
33.7
42.5
38.9
39.8
Spatial verification
58.5
27.6
12.3
70.4
68.7
28.7
49.7
37.3
43.3
43.7
43.6
SuperGlue
61.3
28.3
73.7
38.8
56.2
41.5
44.8
48.5
OmniGlue
59.1
27.8
13.6
73.0
70.8
19.4
44.4
41.1
43.4
43.7
43.7
ELViS
68.8
32.2
80.4
77.9
41.5
59.2
52.3
50.5
53.9
Table H: Comparison with image matching methods. Landmark datasets are treated as in-domain (ID) for all methods, while the remaining datasets are evaluated out-of-domain (OOD).
A
ID
OOD
avg
iterations
ID
OOD
avg
0.01
46.4
47.7
47.4
50.5
53.4
52.7
0.1
50.5
55.0
53.9
3
51.2
54.6
53.7
0.5
50.7
52.1
51.7
5
51.0
54.4
53.6
1.0
48.9
48.2
48.4
10
50.5
55.0
53.9
learnalle
51.2
54.9
54.0
20
51.2
55.0
54.1
Table I: Ablation study of the OT hyperparameters. Models are trained on the Landmarks (GLDv2) domain. We report average performance over the in-domain (ID), out-of-domain (OOD), and all benchmarks (avg). We assess the impact of the regularization parameter and the number of OT iterations, varying hyperparameter at time while keeping all others at their default values.
E IMPACT OF OT HYPERPARAMETERS
Table I reports the performance of ELViS under different values of the regularization parameter and different numbers of OT iterations. For each configuration, we train a new ELViS model from scratch. The results confirm that increasing the number of OT refinement iterations yields slightly better performance. Conversely, setting À to values either higher or lower than the default value (A 0.1) leads to a substantial performance drop, while making À learnable does not provide any noticeable improvement on average. Using different hyperparameters during training and testing degrades performance considerably and was thus omitted from the results.
F DETAILED PERFORMANCE ANALYSIS
In Figure G, we compare the performance per query basis for ELVIS trained in two different domains. What we observe is justified by the train-test domain gaps. In particular, performance on household items is better when training household items than landmarks (left plot) and vice versa (middle plot). On INSTRE, which includes a small number of landmarks and household items among large variety of objects, the best performing model varies lot across queries.
In Figure H, we show the improvement of ELViS re-ranking over global descriptor retrieval, demon- strated per-query basis. For the majority of queries, the model improves the ranking, while the cases where it performs poorly are few and marginally impacted.
G DETAILED QUANTITATIVE RESULTS
In Tables J and Tables K, we provide the mean and standard deviation across all runs. For every setting, we train three models using a different seed each time. These tables partly repeat information provided in the main paper, but are meant to complement the ones from the main paper with the std and the detailed per dataset performance for DINOv3 and SigLIP.
H ADDITIONAL QUALITATIVE EXAMPLES
Figure demonstrates queries from each test set and the impact of re-ranking by ELViS over a global similarity. In Figure J, present additional visual examples of correspondences for positive image
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Published as a conference paper at ICLR 2026
SOP- 1k test dataset
GLDv2 test dataset
INSTRE test dataset
ELViS- SOP
0.5
0.5
0.5
0.5
0.5
0.5
ELViS- GLDvv
ELViS- GLDv2
ELViS- GLDvv
Figure G: Average precision per query. Each marker corresponds to the AP of single query evaluated on different test datasets using DINOv2 as a representation model. x-axis: performance using ELVIS trained on SOP dataset. y-axis: performance using EI ViS trained on GLDvv dataset.
INSTRE test dataset
ELVIS trained on SOP
0.8
0.6
0.2
0
0.2
0.4
0.6
0.8
1
global descriptor without re-ranking
Figure Average precision per query. Each marker corresponds to the AP of single query evaluated on INSTRE using DINOv2 as a representation model. y-axis: performance using ELViS trained on SOP dataset. x-axis: performance of the global descriptor without any re-ranking.
pairs from various domains, and in Figure the corresponding similarity matrices. Note that initial similarity matrix with many large values does not necessarily result in many large values after refinement and strength transformation. This is due to the optimal transport optimization that jointly processes all similarities and requires some kind of mutual compatibility in the final result.
I LLM USAGE IN THIS PAPER
An LLM was used to correct and improve some already written parts of the paper, i.e. in the form of an advanced grammar/syntax checker, and for polishing the phrasing. An LLM was never used to generate text from scratch.
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Published as a conference paper at ICLR 2026
Method
ROxford Pariss
GLDv2 ILISS
INSTRE MET
ProdM RP2K
SOP-1k ID
OOD
DINOv2 descriptors (Oquab al., 2024)
No re-ranking
47.3
67.9
27.3
9.4
65.3
61.6
24.7
39.0
33.7
42.5
38.9
39.8
Chamfer
43.0
54.8
23.8
55.0
37.3
17.8
55.8
40.2
37.6
Chamfer&OT
$5.6
65.5
2188
14.3
76.0
74.0
35.1
55.4
46.2
42.2
50.2
48.2
74.4
13.1
72.4.s
64.1ar>
52.1
48.6..1
49.2am
Forree
63.7.aa
73.4aa
32.6usa
15.2mu
77.6.a
72.0.aa
35.6.a
47.7as
43.7
50.6am
48.6.a2
49.1.m
AMES
65.6.as
74.6ua
34,7ux
14.6am
75.6
70.7uu
32.3m
56.5.a
48.5
52.4m
49.7.x>
50.4
ELVIS
64.3.aa
73.3
$2.2prx
18.8
80.4g>>
76.5er
41.5
59.2gt
$2,3
50.5
$5.0ga>
53.9pm
DINOv3 descriptors (Siméoni et al, 2025)
No re-ranking
59.7
75.4
31.7
26.5
88.2
71.2
64.9
61.6
44.5
49.6
60.5
57.8
Chamfer
34.1
Chamfer+OT
56.1
66.9
21.9
12.3
86.7
63.0
48.8
57.4
40.5
41.7
51.4
49.0
RRT
72.7.a>
82.0ara
25.8at
84.0=
58.3
50.6.9
62.0g>
56.1
56.2
56.4
I2 Former
70.8.e>
80.1ica
36.5
34.3
92.3..:
79.3
62.9ma
63.0.e:
$1.4.m
56.0me
63.9..:
61.9
AMES
75.1
82.2
61.8as
ELVIS
74.0...
79.9
37.0.r
41.2
93.4.
81.0.az
63.9pm
68. Jaxa
57.0ax
56.9am
67.4...
64.8aa
SigL.IP2 deseriptors (Tschannen et al., 2025)
No re-ranking
17.3
47.8
18.3
22.4
89.8
61.7
70.2
40.6
62.2
25.4
51.8
49.7
Chamfer
23.0
22.9
83.7
$7.3
67.1
52.2
ChunfersOT
23.0
54.9
19.7
28.3
90.0
69.0
69.3
50.2
67.3
29.3
62.3
54.1
RRT
28.3.aa
62.0.ca
29.8.s:
24.9.
82.9..a
59.2
65.3
48.4.ss
69.7.sr
37.5mm
58.4.ss
53.2
Former
25.7-a4
59.8
27.7as
31.3atc
90.4-s
69.4.s
70.5at
47.8-1x
68.4
35.2ma
63.0.s:
56.0.t
AMES 27.7-11
61.1aa
29.9aa
30.4aa
89.0.sa
65.9taa
72.5mma
48.2
70.1
37.1aar
62.7.aa
56.3
ELVIS
27.8.
61.5wa
28.1
41.3.z
93.5
76.2..a
73.4uu
55.9.sa
72.1um
36.4aa
68.7...
60.6uu
Table J: mAP mean and standard deviation for training on landmarks (GLDv2), Three different backbones used as representation model.
Method
ROxford RPaars GLDv2
ILIAS
INSTRE
MET
ProdIM
RP2K
SOP-1k
ID
0OD
uvg
No re-ranking
47.3
67.9
27.3
9.4
65.3
61.6
24.7
39.0
33.7
33.7
40.7
39,8
Chanfer
6.7
50.2
Chamfer-OT
43.7
60.8
18.6
11.7
75.9
71.6
37.5
52.7
45.8
45.8
45.7
45.7
RRT
28.2..n
58.7.1a
10.8.s:
12.2
68.4..*
25.5
32.2.ra
46.9.
57.1.s
57.1
342.
37.1as
F'FFormer
43.4.s
67.5..
23.7.aa
12.9
73.4.t.
59.3..:
32.8ita
42.0ws
51.1is
51.1a
42.a.>
43.8mar
AMES
46.2
72.2...
56.7.m.
ELVIS
50.5.r
68.9..
22.9..
18.6
81.1
76.7.
44.1mm
$4.2.s
$4.9ta
54.9.m
$1.0
51.5m
Table K: mAP mean and standard deviation for training on household items (SOP). DINOv2 used as representation model.
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Published as a conference paper at ICLR 2026
AP: 12.6 80.0
152 8
88
65 -
40 ++0
27
17 -
ROxford
68.9
316
218
196
187
128
-4
R.Pris
16
GL.vv2
ILIAS
AP: 17.5 100.0
222 +
214 +
209
195 -
190
INSTRE
AP: 16.2 92.8
283
283 11
280
280
279 -
276
ProdIM
AP: 5.2 91.0
+
135 0
115
82 +
66 0
J2 0
RP2K
84.7
208
SOP-Ik
100.0
100.0
100.0
MET
Figure The impact of re-ranking with ELViS. We show a single query and its database positives per for each set, with the exception of the MET dataset where three queries shown. The text above each query denotes the change in Average Precision. The text above the positive images denotes the number negative images ranked before the positive using only global similarity (1) and after re-ranking with ELViS (2), denoted as (1) (1)- The positives are ordered based on the difference (1)-(2 in descending order.
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Published as a conference paper at ICLR 2026
S
Figure J: Visualization of strong correspondences before (left) and after (right) refinement with optimal transport.
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Published as a conference paper at ICLR 2026
0.0
0.0
0.0
3.0
100
0.0
500
3.0
Figure K: Visualization of similarity matrices before (left) and after (right) refinement with opti- mal transport with individual values passed through function f. A subset of 100 descriptors is used. These examples correspond to the ones of Figure
23


Reviews:
[{"review_id": "Br369ioQiY", "reviewer": "Reviewer_6e19", "summary": "Instance-level image retrieval struggles with poor cross-domain generalization—existing models overfit to domain-specific data and fail on unseen domains, due to scarce cross-domain training data and reliance on representation space.\nSpecifically,\n1.Refine the local descriptor similarity matrix using entropy-regularized optimal transport (OT) with descriptor-dependent dustbin gains to filter uninformative patches\n2.Aggregate global similarity: select strongest local similarities per descriptor, weight them via a learnable function f, and sum—trained with modified BCE loss.", "questions": "For the questions , please refer to the above summary of weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper shifts instance-level image retrieval from representation space to similarity space for stronger cross-domain robustness, refines optimal transport (OT) with descriptor-dependent dustbin gains , and creates the first unified benchmark for single-source cross-domain retrieval—uniting 8 datasets across 5 domains to standardize generalization evaluation.", "weaknesses": "Chowdhury (2022) also employed optimal transport (OT) to address the instance-level image retrieval task. Therefore, I have concerns about the technical innovation of this paper.\n•\tOptimal Transport (OT) Parameters: The paper uses 10 iterations of the Sinkhorn-Knopp algorithm and a regularization term \\(\\lambda = 0.1\\), but it does not explain: i) Why 10 iterations (not 5 or 20)? ii) Why \\(\\lambda = 0.1\\)? Cuturi (2013) shows \\(\\lambda\\) directly impacts OT’s accuracy-efficiency tradeoff\n•\tExtreme Domains: The paper does not evaluate on domains with radical visual differences from natural images (e.g., infrared images, underwater photos, remote sensing imagery)—scenarios where cross-domain retrieval is highly valuable (e.g., satellite image matching for disaster response).\n•\tSmall-Sample Training: The paper uses large training sets (GLDv2 has 762K images, SOP has 60.5K), but many real-world domains have only 100–1000 labeled samples. It is unknown if ELViS’s small parameter count (96K) translates to good small-sample performance.\n\n \nCuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26.\nChowdhury, P. N., Bhunia, A. K., Gajjala, V. R., Sain, A., Xiang, T., & Song, Y. Z. (2022). Partially does it: Towards scene-level fg-sbir with partial input. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2395-2405).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M0GCGQQlo5", "reviewer": "Reviewer_dvx4", "summary": "The paper presents ELViS, a re-ranking technique which is robust across domains. Elvis can be applied on top of common vision foundation models like DINOv2, DINOv3 and SigLIP2, and uses the transformer's local features by applying a lightweight post-processing to output a similarity score between two images, which is then used to re-rank a shortlist of retrieved candidates.", "questions": "See the weaknesses stated above", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "The paper outlines, demonstrates, and tackles a clear problem, which is that re-ranking methods trained on one domain underperform on others.\nElvis shows overall improvement when used on OOD data.\nThe paper is well written", "weaknesses": "1. Can Elvis work when images are of different resolutions? Given that f is an MLP it seems like Elvis would require images at a fixed resolution.\n\n2. I don't fully understand Figure 3. A better caption would be helpful\n\n3. Splitting results on ROxford and RParis would make results clearer and more comparable with other literature. Also which sets of ROxford and RParis are used (easy, medium, hard)?\n\n4. The dataset table should report also the sizes of train/val/test sets for the two datasets used for training\n\n5. A couple of images per dataset would help the reader to understand the domain gap between any two datasets\n\n6. I believe the retrieval is performed with the same model of which the local features are used? I don't see this explicitly stated in the paper\n\n7. Most importantly, comparing with image matching methods would be really helpful for the reader. Are the presented methods relevant, or should methods like SuperGlue be used for re-ranking in these domains?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4FxvwaRwqu", "reviewer": "Reviewer_UYEL", "summary": "This paper introduced a image-to-image similarity model that promotes cross-domain transfer, namely Efficient Local Visual Similarity (ELViS). In order to facilitate faster and more explainable image retrieve in cross-domain scenarios, ELViS leveraged optimal transportation to refine the local description similarity matrix S’, and then aggregate a learnable voting process to transfer the local similarity to the global similarity for the further image retrieval. \nTheir major technical contribution of their work lies in (1) the construction of refined local-description similarity matrix S’, which contains traditional similarity matrix S, data-dependent gains (learned by parametric method) that suppress uninformative descriptors (dustbins) and the learnable scalars that stands for transportation mass for dustbins. (2) the voting mechanism that transfer local descriptions to global description. (3) The authors built a benchmarking protocol that unified 8 existing datasets across various domains.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "(1) The construction of refined local description similarity matrix is novel and intuitive. Especially the introduction of dustbin that avoids the hard comparison between different image instance. And the implementation of optimal transportation to refine the similarity matrix is intuitive and reasonable. Additionally, compared to the former strategy, this paper used the parametric method to empower the model with the ability to adjust the dustbin with self-supervision, making the model more interpretative and flexible. \n(2) They introduced 8 benchmarks that contains various kinds of domains, and they are the first work to conduct such an extensive evaluation of single-source domain generalization in instance-level retrieval. And the model performed well on those datasets, especially on out-of-domain retrieval scenarios.\n(3) Most of the figures and narrations in this paper (except for Introduction) is good and logical, stating their motivation and insights.  \n(4) The experiments and further analysis is abundant and showed the model’s effectiveness.", "weaknesses": "(1) Compared to their technical contribution, their narration in the introduction is less satisfying and cannot specify their motivations. \na. The authors should detailed the reason why the focus on local descriptors is better than the global descriptors for the cross-domain image retrieval. Even though the author mentioned the intepretability and time cost, they can delve deeper into the explanation in representation space, making their statement and motivation less trivial and more solid.\nb. It would be better for the authors to add a figure for this comparison, making their motivation more intuitive.\nc. It would be better for the author to itemize and highlight their contribution in end of the introduction, making their contribution more clear for the readers.\n(2) The authors should simplify the Section.2 (Related Work), instead explicitly detail their motivation, contribution and analysis on previous researches in the introduction.\n(3) Even though model’s in-domain performances didin’t achieve state-of-the-art performances, it is acceptable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zdDN2glxvR", "reviewer": "Reviewer_2TKs", "summary": "This paper introduces ELViS (Efficient Local Visual Similarity), a novel model for instance-level image retrieval. The core problem addressed is single-source domain generalization: training a model on one domain (e.g., landmarks) and having it perform well on retrieval tasks in unseen domains.\n\n**Method Overview:**  \n- Input: Local descriptors are extracted from two images using a foundation model (like DINOv2).  \n - Similarity Matrix Refinement: The local descriptor similarity matrix is refined using Optimal Transport with a key innovation: descriptor-dependent dustbin gains. This allows the model to learn to ignore uninformative descriptors (e.g., from the background).  \n- Vote Aggregation: For each descriptor, the strongest correspondence (similarity) is selected as a `\"vote.\" A small, learned function \\$f\\$ transforms these vote strengths, and they are summed to produce a final, global image similarity score.  \n- Training: The model uses a modified Binary Cross-Entropy loss with a second learned function g to reshape the penalty curve during training, which is discarded at inference.  \n\n\n**Main Results:**  \nThe authors demonstrate that ELViS achieves state-of-the-art performance on a comprehensive benchmark of 8 datasets, showing superior generalization to out-of-domain data while being significantly faster and more parameter-efficient than competing transformer-based methods (RRT, R²Former, AMES).", "questions": "-", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is well written and clear to follow.  \n- SOTA Cross-Domain Generalization: The primary strength of ELViS is its ability to perform robustly on domains unseen during training. It consistently outperforms all competitors on out-of-domain (OOD) datasets.  \n- The method is efficient on both parameters count and latency aspects.  \n- Ablation studies on the contribution of each component is provided and well demonstrated.", "weaknesses": "- Justification for \\$g\\$: The use of the learned function \\$g\\$ in the loss, which is discarded at inference, is an unconventional and somewhat non-standard technique. While it works well empirically, a more rigorous theoretical explanation for why this is necessary and why discarding it is valid would strengthen the method.  \n- Performance on In-Domain Data: In some in-domain (ID) settings, ELViS is outperformed by other methods. This suggests that while its bias towards generalization is powerful, it might come at the cost for optimal performance on the training domain itself.  \n- Novelty: The method primarily relies on existing components, which somewhat limits its degree of novelty.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)