Title: CVGL: Causal Learning and Geometric Topology
Venue: NeurIPS 2025
Accepted: yes
Keywords: cross-view

casual learning

BEV
Abstract: Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it with a corresponding aerial image. This is critical for autonomous navigation and mapping in complex real-world scenarios. However, the task remains challenging due to significant viewpoint differences and the influence of confounding factors. To tackle these issues, we propose the Causal Learning and Geometric Topology (CLGT) framework, which integrates two key components: a Causal Feature Extractor (CFE) that mitigates the influence of confounding factors by leveraging causal intervention to encourage the model to focus on stable, task-relevant semantics; and a Geometric Topology Fusion (GT Fusion) module that injects Bird’s Eye View (BEV) road topology into street features to alleviate cross-view inconsistencies caused by extreme perspective changes. Additionally, we introduce a Data-Adaptive Pooling (DA Pooling) module to enhance the representation of semantically rich regions. Extensive experiments on CVUSA, CVACT, and their robustness-enhanced variants (CVUSA-C-ALL and CVACT-C-ALL) demonstrate that CLGT achieves state-of-the-art performance, particularly under challenging real-world corruptions.

CVGL: Causal Learning and Geometric Topology

Abstract

Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it with a corresponding aerial image. This is critical for autonomous navigation and mapping in complex real-world scenarios. However, the task remains challenging due to significant viewpoint differences and the influence of confounding factors. To tackle these issues, we propose the Causal Learning and Geometric Topology (CLGT) framework, which integrates two key components: a Causal Feature Extractor (CFE) that mitigates the influence of confounding factors by leveraging causal intervention to encourage the model to focus on stable, task-relevant semantics; and a Geometric Topology Fusion (GT Fusion) module that injects Bird's Eye View (BEV) road topology into street features to alleviate cross-view inconsistencies caused by extreme perspective changes. Additionally, we introduce a Data-Adaptive Pooling (DA Pooling) module to enhance the representation of semantically rich regions. Extensive experiments on CVUSA, CVACT, and their robustness-enhanced variants (CVUSA-C-ALL and CVACT-C-ALL) demonstrate that CLGT achieves state-of-the-art performance, particularly under challenging real-world corruptions. Our codes are available at CLGT.

1 Introduction

Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it to a corresponding aerial image. This task plays a crucial role in applications such as autonomous driving, robotic navigation, and urban mapping [24; 2; 27]. However, it remains highly challenging due to the extreme differences in perspective, scale, appearance, confounders and occlusion between street and aerial views. Previous studies have mainly explored three directions to improve cross-view matching: viewpoint modeling [28], spatial alignment [21], and hard negative mining [1]. Despite these efforts, cross-view

street space

C

D

𝑿𝒔

𝑿𝒂

aerial space

𝑭𝒄

Z

𝑭𝒏

Y

𝑓(𝑋)

𝑓(𝑋)

D

domain

C

content

𝑿𝒔

street image

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aerial image

network

𝑓(∙)

Z

𝑭𝒄

𝑭𝒏

feature space

causal features

non-causal features

labels

Y

geo-localization remains challenging due to weather changes, misalignment, and occlusions, all of which demand stronger generalization and discriminative feature learning. To address these limitations, Mi et al.[13] introduced feature consistency constraints to enhance robustness to orientation and field-of-view variations. To better reflect real-world conditions, Zhang et al.[32] proposed corruption-rich benchmarks for robust evaluation, while Ye et al.[28] leveraged a Bird's Eye View (BEV) representation as an intermediate domain to bridge the large cross-view gap.

Low

Middle

High

Origin

Building upon the aforementioned challenges and recent advances, we propose a framework to address cross-view geo-localization from both causal perspectives and geometric. Inspired by instances where classification models mistakenly associate sheep with grass, we argue that the CVGL task should not rely on confounding factors such as background or lighting. To mitigate the interference of confounding (non-causal) factors and spurious correlations, while enhancing model generalization, we introduce causal learning concepts into the CVGL task. Drawing on previous causal modeling work and considering the characteristics of street images, we establish the first Structural Causal Model (SCM) for CVGL, and perform causal intervention guided by the SCM. Since domain-specific (non-causal) signals often reside in extreme low and high frequency bands, while mid-frequency components typically preserve structure-relevant, discriminative information [7] (Figure 2), we design our Causal Feature Extractor as a do-operation in the frequency domain. This allows us to implement a back-door adjustment -akin to causal interventions -that increases the model's attention to causal factors while reducing interference from non-causal factors. To further enhance the model's geometric awareness and mitigate the impact of large viewpoint gaps, we propose the Geometric Topology Fusion (GT Fusion) module, which robustly integrates BEV road topology into street features, leveraging clearer and more localized road topology compared to complex street images.

At the feature level, conventional pooling layers often fail to capture rich semantic cues. To address this, we propose a DA Pooling module that dynamically refines feature representations, enabling the model to capture more context-aware information across diverse scenes and viewpoints.

In summary, our main contributions are:

We are the first to introduce causal learning concepts into CVGL tasks by applying causal interventions to latent confounding factors, thereby reducing their influence on feature learning. This mechanism enables the model to focus on causally relevant information, such as building structures and road layouts, leading to improved robustness and generalization in complex environments.

We propose a GT Fusion module that enhances the model's ability to perceive geometric information, mitigating the issue of large viewpoint discrepancies in CVGL tasks and providing more robust localization performance for CVGL.

We design a DA Pooling to extract rich semantic information and enhance semantic representations across different environments.

Our work highlights the importance of structural reasoning and causal robustness in bridging the cross-view domain gap, setting a new direction for future research in geo-localization.

2 Related Work

2.1 Cross-view Geo-localization

Contrastive Learning-based methods. Contrastive learning has been widely applied in crossview geolocation tasks [1; 25; 26; 21; 28; 31]. It helps mitigate feature distribution discrepancies between different viewpoints. For example, ConGeo [13] leveraged both single-view and crossview contrastive losses while incorporating view-specific augmentation strategies. This effectively extracts robust feature representations, enabling the model to maintain high matching accuracy despite viewpoint limitations and orientation deviations. Moreover, Sample4Geo [1] proposed a simplified yet effective contrastive learning framework with a symmetric InfoNCE [19] loss, which fully utilizes all negative samples to accelerate model convergence.

Incorporation of Geometric Information. To address the challenges posed by drastic viewpoint variations, some approaches focused on extracting geometric layout information or leveraging BEV images to enforce geometric consistency constraints. For instance, GeoDTR [33] employed a geometric layout extractor to learn spatial correlations between aerial and street features, preventing overfitting to low-level details. Similarly, EP-BEV [28] and HC-Net [21] integrated BEV representations into cross-view geolocation to bridge the substantial differences between views. EP-BEV utilized a dual-branch structure to impose geometric consistency constraints, while HC-Net [21] directly reformulated cross-view geolocation as an image alignment problem.

2.2 Causality in Computer Vision

This limitation underscores the motivation for causal inference in visual learning: relying solely on statistical correlations in data is insufficient for reliably predicting counterfactual outcomes and may amplify spurious associations. Causal inference, by modeling the underlying data-generating mechanisms, aims to isolate invariant causal factors and thereby enhance generalization to unseen domains and conditions. To mitigate the interference of non-causal features and extract invariant causal representations, causal mechanisms have been widely adopted in computer vision [18; 3; 17; 30; 6].

In cross-view geo-localization tasks, it is essential to first establish causal relationships and then apply causal inference-including interventional estimation and counterfactual analysis-to eliminate confounding contextual factors and domain shifts. This approach enhances model robustness against domain variations and weather conditions, which pose significant challenges in this task. Drawing on previous causal modeling work [12; 10] and considering the characteristics of street images, we formally define the causal relationships cross-view geo-localization as shown in Figure1, and employ interventional estimation to block the direct influence of confounders, significantly improving generalization. There are two common methods for causal intervention: front-door adjustment and back-door adjustment. Front-door adjustment is used when non-causal factors (confounders) are unobserved, requiring the introduction of an intermediate variable M to reduce the influence of confounding factors. When confounders are observable, back-door adjustment is used, where confounding factors are directly intervened to reduce their impact.

3 Method: CLGT

This paper proposes a novel framework for cross-view geo-localization. A multi-head attention-based fusion module, which robustly integrates BEV features into street features via cross-attention and dual dynamic fusion, enforces geometric consistency constraints. To enhance causal features in street representations while mitigating the interference of non-causal features, we employ causal inference-based estimation and intervention. Furthermore, we introduce a DA pooling module to refine the fused features with rich semantic information. The overall model architecture is shown in Figure 3. The following sections provide a detailed introduction to our proposed method.

3.1 Preliminary

BEV Generation. Various methods exist for generating BEV images, including geometry-based transformations [21; 28], Transformer-based [35], and diffusion-based methods [29]. To balance

Feature

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Geometric Topology Learning

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Query*

efficiency and memory use, we adopt the geometric transformation from [21], which directly computes BEV point positions via geometric back-projection from panoramic images. This explicit mapping projects street-view images into BEV space without relying on depth estimation or camera parameters, enabling a simple and efficient street-to-BEV transformation.

Structural Causal Model. As illustrated in Figure 1, our SCM is grounded on the following assumptions:

The street image X s is mainly generated from two sources: semantic content C and a domain confounder D (e.g., background, lighting), denoted as C → X s ← D .

The content C contains both discriminative and non-discriminative parts. Together with D , the non-discriminative part contributes to the generation of non-causal features F n via D → F n ← C . The CFE module perturbs a portion of these non-causal components.

The causal features F c are derived from the discriminative part of C via C → F c .

The full feature representation Z = { F c , F n } influences the final prediction Y via Z → Y , where Y is the matching label.

The overall cross-view matching process can be expressed as X a → f ( X ) → Y ← f ( X ) ← X s , where X a denotes the aerial image. In this context, the SCM formulation Z → Y is a causal abstraction of the model's computational path X s → f ( X ) → Y .

3.2 Causal Learning

The complete process of Causal Learning is illustrated in the top-right corner of Figure 3, where Query ∗ is obtained through the Causal Features Extractor.

Causal Features Extractor. As shown in Figure 2, roads and buildings in street-view images tend to occupy the mid-frequency spectrum, while style variations are concentrated in the high and low ends, respectively. This aligns with the nature of CVGL, where structural elements are crucial for localization, and view-specific cues often act as noise. To isolate task-relevant features, we leverage the Discrete Cosine Transform (DCT) in our Causal Feature Extractor. Then our Content-aware Mask (CaM) constructs three concentric circular masks with initial radii of r 1 , r 2 , and r 3 , dividing the frequency spectrum into four regions. Unlike prior work [23] that used fixed spectral thresholds, these radii are linearly increased based on image gradient magnitude (via Sobel operator), so that images with stronger gradients preserve more mid-frequency components, enabling better retention of causal information. Larger radii correspond to stronger Gaussian perturbations in outer frequency bands. This allows the model to adaptively preserve mid-frequency, causal components while suppressing non-causal signals. The masked frequencies are then transformed back via inverse DCT. The entire

Causal Feature Extractor

Query image

Causal image

Gaussian function

DCT

Inverse  DCT

Frequency domain

CaM

Non-causal factors

Causal factors

+

process of CFE is shown in Figure 4. The Causal Features Extractor is defined as:

where F denotes the Discrete Cosine Transform and F -1 is its inverse. M ( r ) is a content-aware circular band-pass mask with radius r . G ( · ) denotes a randomization function, defined as G ( X ) = X · (1 + N (0 , 1)) .

After obtaining Query ∗ through the CFE module (do ( X s := X ∗ s ) ), we impose a supervision loss between the causally enhanced features derived from Query ∗ and the fused features to weaken the path C → X s → f ( X ) → Y (where C denotes confounding variables that influence the generation of X s ), achieving the effect similar to back-door adjustment in causal interventions.

3.3 Geometric Topology Learning

To guide the fusion of street and BEV features, we propose the GT Fusion module. This module effectively leverages the BEV road topology information while enriching the street features output by the backbone, dynamically injecting road topology information without compromising the street details.

GT Fusion. As shown in Figure 5 (left), our fusion module first applies a 3 × 3 depthwise convolution to backbone outputs X b , X s ∈ R C × H × W to extract local features, maintaining the feature shape. To capture global context, instead of the common Spatial Reduction Attention (SRA) [11], which disrupts boundary spatial structure via nonoverlapping token reduction, we adopt Overlapping Spatial Reduction (OSR) to preserve spatial coherence. Finally, X s acts as the query and X b as key and value in a cross-attention module, effectively fusing street-view and BEV features.

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(GT Fusion)

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Dot Product

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Inspired by [4], we further introduce a Dual Dynamic Fusion (DDF) strategy to robustly integrate the original street features (denoted as F s ) and

their geometry-enhanced counterparts(denoted as F g ). DDF is defined as:

where F s denotes the original street-view feature. W is a 1 × 1 convolution, while Conv refers to a 3 × 3 convolution. [ · , · ] denotes the concatenation operation along the channel dimension. γ is a linear transformation, and σ denotes the sigmoid activation function. The GT Fusion module can be formulated as follows:

where proj refers to the 3 × 3 depthwise convolution, with X s and X b representing the street and BEV features output by the backbone, respectively. LM denotes a Layer Normalization. OSR stands for Overlapping Spatial Reduction, which is used for global information extraction. B is a relative position bias matrix that encodes the spatial relationships within the attention maps, d represents the number of channels in each attention head, and DDF denotes a dual dynamic fusion module.

Data-Adaptive Pooling. As shown on the right side of Figure 5, our Data-Adaptive Pooling module improves upon conventional pooling methods such as global max pooling, global average pooling, and adaptive pooling. These traditional pooling techniques, when used as the final feature aggregation step, fail to effectively capture the rich semantic information of the features. To enhance the representation capability, we combine global max pooling, global average pooling, and geometric mean (Gem) pooling into a single Gate Pooling module. This allows the model to autonomously learn the pooling output, and improve the quality of the final token, thus enhancing both the model's representational power and robustness, which can also be formulated as:

where MaxPool is global max pooling, AvgPool is global average pooling, Gem denotes a geometric mean pooling, Linear is a Linear Functiion, and Gate denotes a gating mechanism.

3.4 Loss Function

We apply InfoNCE loss between the fused features and the aerial image features, which serves as the primary supervision signal to optimize our model. The InfoNCE loss is defined as:

where f denotes the fused feature guided by the query street image, and S is the set of encoded aerial images with one positive r + matching f . The InfoNCE loss computes the dot-product similarity between f and each r i , maximizing similarity with r + and minimizing it with negatives. The temperature τ controls distribution sharpness and can be fixed or learned.

As stated in Section 3.2, we apply InfoNCE loss between the causally enhanced features and the fused features to achieve a similar effect to back-door adjustment, encouraging the fused features to focus on causal components. Prior to fusion, to encourage the BEV and street features to lie in a geometrically consistent space, we also apply InfoNCE loss between the two. To preserve their complementarity, we apply a scaling factor to control their learning balance. Thus, we obtain the overall loss of CLGT by computing the weighted sum of them as follows:

where α and γ are scaling coefficients, s ∗ denotes the causally enhanced street features, s represents the original street features, and b is the BEV feature.

4 Experiment

In our evaluation we conduct experiments on four standard benchmarks, namely CVUSA [22], CVACT [8], VIGOR [36] and CVACT_val-C-ALL, CVACT_test-C-ALL, CVUSA-C-ALL [32]. In the subsequent tables we compare our approach with previous work.

4.1 Dataset and Evaluation Protocol

Dataset. We evaluate our model on three widely-used cross-view geo-localization benchmarks-CVUSA, CVACT, and VIGOR-as well as their robust variants: CVACT_val-C-ALL, CVACT_test-C-ALL, and CVUSA-C-ALL, which introduce various real-world corruptions to test model robustness under challenging conditions. CVUSA and CVACT each provide 35,532 training and 8,884 testing image pairs with a strict 1-to-1 ground-to-aerial correspondence. In addition, CVACToffers an extra 92,802 GPS-tagged query images for large-scale retrieval evaluation, making it suitable for both standard and large-scale testing scenarios. VIGOR is a more challenging benchmark that spans four metropolitan areas-New York, Seattle, San Francisco, and Chicago-and includes 105,214 query and 90,618 reference images. Unlike CVUSA and CVACT, VIGOR introduces a harder retrieval setup by assigning each query one true positive and three semi-positive samples, thus increasing the difficulty of discriminative matching. It also supports both same-city and cross-city evaluation settings to assess generalization. To further assess model robustness in realistic conditions, we employ corruption-augmented datasets: CVACT_val-C-ALL, CVACT_test-C-ALL, and CVUSA-C-ALL. These variants simulate 16 types of visual degradations, generating approximately 1.5 million corrupted images in total. They provide a rigorous benchmark for evaluating the model's ability to maintain performance under various environmental and sensor-induced perturbations.

Evaluation Protocol. We adopt Recall@K as the primary evaluation metric, where K ∈ { 1 , 5 , 10 } , as well as Recall@1%. A query is considered correctly localized if its corresponding aerial image appears among the top-K retrieved candidates for a given street panorama.

4.2 Implementation Details

During the retrieval stage, we adopt ConvNeXtBas the backbone encoder for both street images and aerial images. Our baseline is EP-BEV. To reduce computational cost and memory usage, we set the image resolution to 384 × 384 , consistent with EP-BEV. The model is optimized using AdamW with an initial learning rate of 0 . 5 × 10 -3 . We train the network for 40 epochs with a batch size of 128. The training is conducted on eight 32GB NVIDIA V100 GPUs. For both α and γ in Equation 9, we set their values to 0.1 to provide auxiliary supervision

without overwhelming the main optimization objective. When we increase the value of γ , the model performance improves across various datasets. However, to prevent the value from becoming too large and causing model collapse, which would negatively affect the matching between street and aerial images, we set a default value of 0.1, although this collapse was not observed during training. The optimal value is 0.5, and we will also provide hyperparameter experiments and model performance with γ = 0 . 5 in the supplementary materials. We set the initial three radii for the content-aware mask to 0.1, 0.3, and 0.6, respectively. We also observe that performance is stable under small variations in the initial radius. Other training settings follow those used in Sample4Geo.

4.3 Comparing with State-of-the-art Models

Cross-view Image retrieval. As shown in Table 2, our method achieves the best overall performance across CVUSA, CVACT_val, and CVACT_test datasets. Compared with the strong baseline EP-BEV, our model improves Recall@1 from 97.41% to 98.85% on CVUSA, and from 90.61% to 91.97% on CVACT_val. On the more realistic CVACT_test set, we achieve 73.22% Recall@1, surpassing EP-BEV by 1.81% points.These consistent gains demonstrate the effectiveness of our design. The

integration of BEV-based geometric topology helps capture structured layout cues, while the causal learning strategy improves robustness by suppressing spurious visual signals. Together, they enable more discriminative and generalizable representations for cross-view geo-localization. Results on the VIGOR dataset are provided in the supplementary materials.

Robustness Evaluation. As shown in Table 3, our method consistently outperforms baselines across robust datasets, with an average improvement of 5.00%. Notably, it achieves a 6.62% gain on CVUSA-C-ALL, highlighting the model's ability to extract localization-relevant cues such as edge textures of buildings and road structures, while suppressing non-causal noise like lighting and weather conditions. On challenging splits such as CVACT_val-C-ALL, CVACT_test-C-ALL, and CVUSA-C-ALL, our approach demonstrates strong robustness by mitigating the impact of 16 common perturbations and improving retrieval accuracy. Furthermore, in cross-dataset evaluation (trained on CVUSA, tested on CVACT), our method improves performance by 5.50% and 2.84% (Table 6), further validating its generalization and robustness under distribution shifts.

Ablation Study. We conduct comprehensive ablation studies on CVACT_val, CVACT_val-C-ALL, and CVACT_test-C-ALL to evaluate the individual contributions of each proposed component. As reported in Table 5, employing only the Geometric Topology Fusion (GT Fusion) module

yields a Recall@1 of 91.01% on CVACT_val. This result highlights the importance of geometric consistency learning, which enables the model to better capture road layouts and spatial structures, thereby improving the alignment between ground-level and aerial images.When using only the CFE module, the model achieves a Recall@1 of 91.24%, illustrating its capability to suppress spurious correlations and guide the model toward learning causally relevant and semantically stable features. This enhancement plays a crucial role in resisting interference from latent confounders. As shown in Table 4, our DA Pooling consistently outperforms traditional pooling schemes across all evaluation metrics. Specifically, compared to GeM, DA Pooling improves Recall@1 on CVACT by +2.37%, showing its advantage in dynamically emphasizing informative spatial regions. This confirms the importance of adaptivity in multi-view feature aggregation.

Furthermore, as shown in Table 1, causal learning alone brings significant improvements on the more challenging and corrupted test sets: Recall@1 increases by 2.23% on CVACT_val-C-ALL and by 3.20% on CVACT_test-C-ALL. In addition to Recall@1, other evaluation metrics such as Recall@5 and Recall@10 also show consistent improvements, closely approaching the performance of the full CLGT model. These results confirm the effectiveness of our causal learning strategy in improving robustness under real-world corruptions and diverse input conditions. Overall, the ablation results validate that both modules-GT Fusion and CFE-contribute meaningfully and complement each other in addressing the challenges of cross-view geo-localization.

Visualization Analysis. To qualitatively assess the effectiveness of CLGT, we visualize attention heatmaps generated by the baseline and CLGT models on test images from the CVUSA dataset. As shown in Figure 6, we first visualize the heatmaps on clean images. It can be seen that the baseline model's attention is more scattered, even focusing on the sky and other background noise, while CLGT consistently attends to task-relevant information, especially road structures. Under heavy snow conditions, compared to the baseline, the regions attended by our model remain almost unchanged, whereas the baseline's focus is completely misaligned. This shows that our CLGT consistently attends to semantically meaningful structures, such as road intersections and corner layouts, which are more stable across views. This demonstrates the effectiveness of our design in guiding the model to prioritize task-relevant features and suppress distractions, leading to improved crossview discriminability. Visualizations for other corruption types can be found in the supplementary materials.

Complexity Analysis. As shown in Table 7, we report both GFLOPs and average inference time (in milliseconds per batch of 128 images) on the CVACT_val set. The results demonstrate that our method achieves the best R@1 accuracy with only marginal computational overhead compared to other methods.

Ours

Baseline

Clean

Snow

5 Conclusion and Future Work

In this work, we present a novel cross-view geo-localization framework that integrates BEV-street view fusion with causal learning mechanism. Unlike previous methods that utilize BEV merely as an auxiliary representation, our approach enables feature-level interaction that effectively and robustly incorporate road topology. To further improve generalization, we introduce a causal intervention module, thereby enhancing filters out non-causal information and enhances model robustness under various conditions. Experimental results on both standard and challenging datasets demonstrate consistent performance gains. Nonetheless, the BEV representations derived from geometric transformations contains considerable noise, which limits further the performance improvements. Future work will explore more advanced causal inference strategies tailored to the dynamics of complex cross-view localization tasks.

6 Acknowledgments

This work was supported in part by Shenzhen Science and Technology Program under Grant JCYJ20240813142510014 and Grant 20220810142553001, in part by the Key Project of Department of Education of Guangdong Province under Grant 2023ZDZX1016, and in part by the National Natural Science Foundation of China under Grant 62072318 and Grant U22A2097.

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Yuanze Xu, Ming Dai, Wenxiao Cai, and Wankou Yang. Precise gps-denied uav self-positioning via context-enhanced cross-view geo-localization, 2025. URL https://arxiv.org/abs/ 2502.11408 .

Hongji Yang, Xiufan Lu, and Yingying Zhu. Cross-view geo-localization with layer-to-layer transformer. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems , volume 34, pages 29009-29020. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper_files/ paper/2021/file/f31b20466ae89669f9741e047487eb37-Paper.pdf .

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Junyan Ye, Zhutao Lv, Weijia Li, Jinhua Yu, Haote Yang, Huaping Zhong, and Conghui He. Cross-view image geo-localization with panorama-bev co-retrieval network, 2024. URL https://arxiv.org/abs/2408.05475 .

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel framework for cross-view geo-localization. The primary goal is to improve matching accuracy between street-level and aerial images, especially under challenging real-world conditions.  The authors introduce three main components, namely GT Fusion, CFE and DA-Pooling module for this task, and the proposed method is evaluated on several standard and corruption-augmented benchmarks, where it demonstrates state-of-the-art performance, particularly in robustness against visual corruptions.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n- **Originality**: The introduction of a causal learning paradigm to CVGL is a significant conceptual contribution, establishing a new research avenue by framing robustness as the mitigation of confounders.\n\n- **Significance & Quality**: The extensive experimental evaluation shows compelling state-of-the-art results, especially on corruption-augmented datasets, providing robust empirical evidence for the proposed components' efficacy.\n\n- **Clarity**: the manuscript is generally well-written.\n\n# Weaknesses\n\n- The paper's central claim of applying causal inference seems undermined by the tenuous link between the formal SCM and the CFE's implementation. The manuscript does not establish how the CFE's frequency-filtering constitutes a principled do()-intervention. Consequently, the method appears to be an effective frequency-based augmentation presented with causal terminology, rather than a rigorous application of causal inference.\n\n- The CFE operates on an simplified heuristic that causal and non-causal features are separable in the frequency domain (lines 139-142) with only qualitative supports. The paper lacks substantive analysis to validate this critical assumption, leaving the CFE's foundation to appear more ad-hoc than theoretically grounded and thereby undermining its claim to causality.\n\n- The manuscript omits analysis of inference time and computational cost, precluding an assessment of the method's practical viability for applications like autonomous navigation.\n\nQUESTIONS:\n1. The paper's central claim is predicated on a causal framework, yet the connection between the SCM (Figure 1) and the CFE is not explicit. Please bridge this gap: How does the do()-operation translate to Equation 1? Why is this a principled causal intervention?\n\n2. Could you provide a comparative analysis of inference time and computational overhead against baselines and other top methods?\n\n3. The CFE relies on the heuristic that factors are separable in the frequency domain. Could you provide a more rigorous analysis or empirical validation for this assumption?\n\nIf the authors can convincingly address them, It would be likely that I raise my score.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the author introduces Causal Feature Extractor, Geometric Topology Fusion via BEV, and DA Pooling for cross-view matching. \nMy main concern is the design of Structural Causal Model. More illustrations are needed.\nFigure 3 is confusing. What is structural causal components and redundant non-causal components in the figure 3 ?\nThe Causal Feature Extractor is a data-augmentation method, but it is not neccessary better than the conventional data agumentation. \nThe Geometric Topology Fusion is similar to Qformer, which leverage street feature as query, and the transformed BEV feature as Key and Vale. \nThe DA pooling is incremental to use three kinds of pooling together.\n\nSTRENGTHS AND WEAKNESSES:\nIn this paper, the authors propose a novel framework for cross-view geo-localization that integrates a Causal Feature Extractor (CFE), Geometric Topology Fusion (GT Fusion) using BEV, and a Data-Adaptive Pooling (DA Pooling) module.\n\nStrengths:\n\n1. The paper addresses a timely and challenging problem in cross-view geo-localization and presents a complete pipeline with causal and geometric components.\n\n2. The experimental results are strong across multiple datasets, including corrupted variants, demonstrating robustness.\n\n3. The motivation for introducing causal learning to suppress non-stable features is well-explained.\n\nMain Concerns:\n\n1. Clarity and Justification of Structural Causal Modeling (SCM):\n\nThe design of the Structural Causal Model (SCM) is briefly shown in Figure 1 but lacks sufficient justification and explanation. It remains unclear how the variables in the causal graph are defined and how interventions are operationalized in practice.\n\nFigure 3 refers to “causal” and “non-causal” components based on DCT frequency bands. However, the distinction appears heuristic and lacks empirical or theoretical justification. How confident can we be that mid-frequency bands capture \"causal\" structures while high/low frequencies are \"non-causal\"? More concrete illustrations or validation would be helpful.\n\n2. Causal Feature Extractor as Frequency Filtering:\n\nThe Causal Feature Extractor appears to perform a kind of adaptive frequency-domain filtering with randomized augmentation. While it is framed as causal intervention, the current formulation is arguably closer to frequency-based data augmentation or regularization. The causal interpretation should be further grounded or clarified.\n\n3. Comparison with Standard Augmentations:\n\nWhile the causal feature extractor is shown to improve robustness, it's not sufficiently compared with more standard or learned data augmentation techniques (e.g., RandAugment, CutMix, or recent frequency-based domain generalization approaches). This weakens the claim that causal modeling offers unique benefits.\n\n4. Similarity to Existing Architectures:\n\nThe GT Fusion module resembles prior cross-attention mechanisms such as Q-former, especially the use of street features as queries and BEV features as key/value. The novelty in the geometric fusion design seems marginal unless better distinguished from prior works.\n\n5. DA Pooling Contribution is Incremental:\n\nThe DA Pooling module combines standard pooling types (max, average, and geometric mean) with gating. This is a reasonable design choice but seems incremental in isolation. Ablation results show marginal gain; a clearer motivation or design rationale could strengthen its necessity.\n\n6. Reproducibility and Clarity of Presentation:\n\nSeveral parts of the method, especially loss terms and the frequency masking strategy, are mathematically specified but not intuitively explained. More illustrations and conceptual diagrams would help readers understand the pipeline.\n\n7. The paper title is different from the title in the Openreview system. \n\n8. Cross-view matching. It would be test on drone-view datasets, like University-1652 and Vigor.\n\nQUESTIONS:\nPlease see the weakness.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles cross-view geo-localization which consists in matching street view images to aerial images. This is a challenging task due to the large viewpoint changes between street and aerial images, significant illumination changes as well as potential occlusions. To deal with condition variations and the dynamic nature of the scenes, the authors introduce a causal feature extractor that isolates features relevant for localization such as structural elements while removing distracting features. The causal feature extractor predicts a content aware frequency mask to separate relevant/non-relevant features in the frequency space. Furthermore, street view images are lifted to bird eye view and the respective features from both perspectives are merged through the geometric topology fusion module which creates more robust and discriminative feature maps through. Subsequently, global descriptors are pooled from the fused feature maps through a data-adaptative pooling layer which focuses on semantically rich regions. The resulting localization pipeline is evaluated on two cross-view datasets and their variants which include.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1) The causal learning mechanism is an effective way of injecting causal information in the feature representation. In addition, the causal feature extraction is an elegant way of dissociating semantically important features from distractors without relying on third party pretrained models for detection or condition correction. Its effectiveness is supported by ablation studies. \n2) Augmenting street features with BEV features gives a complementary perspective and makes the resulting representation more robust wrt viewpoint changes and more discriminative which is particularly relevant in the context of cross-view geo-localization. \n3) The proposed representation shows consistent accuracy improvements over prior cross-view geo-localization baselines across all datasets.\n\n### Weaknesses\n1) The causal feature extraction comes from [21] which limits the contributions of the proposed method. The authors state that the difference lies in their content aware frequency mask as opposed to the fixed thresholds used in [21]. However no explanation is provided as to how the content aware mask is predicted. As it is an important part of the paper, this ought to be explained and the benefits of the content aware mask compared to a fixed bandwidth should be shown.     \n\n2) I am skeptical about the benefits of the proposed data-adaptive pooling layer. The geometric mean pooling is already a generalization of global average and max pooling layers, where the learnable parameter balances between max and average pooling behaviors (as such GeM is already “data-adaptive”). I do not see any ablation study to justify the design choice of the proposed data-adaptive pooling layer. \n\n3) While I understand the motivation behind fusing street/BEV features I find the following sentences confusing and it is hard to tie them to the GT fusion module’s architecture.\nl.113 “A multi-head attention-based fusion module integrates BEV features into street features, enforcing geometric consistency constraints”, how can multi-head attention enforce geometric constraints?\nl.151 “effectively leverages the BEV geometric consistency constraints”, what does the geometric consistency constraints refer to here?\nl.152 “dynamically injecting road geometric topology information”, is geometric topology the right term to describe scene layout, doesn’t street view image then also contain geometric topology information?\nDescription of Fig. 4 “ DFF to inject BEV’s geometric topology”, isn’t the BEV features already integrated through the MHA layers\nThe GT fusion module should be more ablated and tied to the textual explanations. Ablations could include, w/o DDF, w/o OCR, maybe basic fusion mechanisms (MHA, concatenation, summation).    \n\n4) Missing reference for InfoNCE: Oord, Aaron van den, Yazhe Li, and Oriol Vinyals. \"Representation learning with contrastive predictive coding.\"\n\nQUESTIONS:\nOverall the motivation behind introducing the street/BEV fusion and the causal mechanism are sound and the resulting method is effective and brings small accuracy gains. However given the high accuracy of the baseline method (EP-BEV) this paper is based upon and the limited novelty, some more explanations and ablations would be required to better grasp the significance of the contributions. (cf weaknesses 2 and 3).  \n\nl144.  “our method dynamically adapts to each input by generating a content-aware frequency mask”, does this refer to the radius of the band-pass mask, how is r predicted?\n\nWhat is the benefit of aligning the street and BEV features through the contrastive loss since the street/BEV features are fused anyway in the subsequent GT fusion module? Also this loss would be applied on the pixel level for dense features, do you randomly sample negative features in the image space?\n\nCould you provide a run time comparison with other baselines both for training and inference?\n\nCf. weakness 3)\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 1, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to address the cross-view geo-localization task where a street-view query image is matched with an extensive database of aerial photos. The drastic viewpoint change, weather change, misalignment, and occlusion between the ground and aerial views make the task challenging. To address these challenges, the authors in this work propose a Causal Learning and Geometric Topology (CLGT) framework, which consists of two novel architectural contributions - a causal feature extractor to focus on stable and task-relevant features, and GT fusion to efficiently inject the Bird’s Eye View (BEV) feature with road topology. The paper also proposes data-adaptive pooling, which outperforms conventional global max pooling, global average pooling, and adaptive pooling on the task at hand. State-of-the-art results on four popular benchmarks prove the efficacy of the proposed CLGT framework.\n\nSTRENGTHS AND WEAKNESSES:\n(1) The paper is well-written and easy to digest. The authors discuss the challenges of the cross-view geo-localization task and systematically propose the model components to solve them. The architectural details are well-documented. \n(2) The strongest point of this paper is the effective results - there are several previous works on cross-view geo-localization reporting results on the CVUSA and CVACT datasets. This paper clearly achieves a state-of-the-art score on the majority of the metrics.\n\nPlease find the detailed weaknesses in the 'questions' section.\n\nQUESTIONS:\n(1) The ablation studies in this paper can be improved. I am interested in seeing a systematic comparison of (a) Causal Features Extractor vs Features using an off-the-shelf pre-trained encoder, (b) GT fusion vs normal cross-attention based fusion, and (c) data-adaptive pooling vs traditional global average/max pooling. I would like to know the author's opinion on which model component plays the most crucial role in improving performance. The authors have reported ablative experiments in Table 5, but it is not clear to me what the model exactly looks like for the rows 'w/o GT Fusion' and 'w/o CFE', i.e., when GT fusion and CFE are not used. How are the features extracted and fused in these cases?\n\n(2) The paper lacks sufficient implementation and hyperparameter details. Which hyperparameters contribute most to the training stability? \n\n(3) Another central area of improvement is the qualitative results. The authors should explicitly demonstrate examples where the proposed architectural components outperform previous methods. Since the primary contribution of this work is the novelty of the model, we should examine where the proposed system succeeds but previous systems fail.\n\n(4) I acknowledge the paper achieves SOTA results, but the authors have missed multiple important baselines (including some already published in recognized venues). I request that the authors conduct an extensive literature review and report all peer-reviewed and published baselines that have already been established.\n\n(a) Cross-View Image Sequence Geo-localization. WACV 2023.\n(b) GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement. TAPMI 2024.\n(c) Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization. CVPR 2024.\n(d) Robust Cross-View Geo-Localization via Content-Viewpoint Disentanglement. arXiv 2025.\n(e) Where am I? Cross-View Geo-localization with Natural Language Descriptions. arXiv 2025.\n\n\nDue to these limitations, I recommend a 'Borderline rejection' at this stage, and I will wait for the authors to address the points (1), (2), and (3). I understand that the rebuttal timeframe is limited, but having a clearer picture of which model components are most effective is essential. I will be willing to increase the score if the issues are well-addressed.\n\nLIMITATIONS:\nThe paper does not include any discussion about the failure modes of the proposed system. The authors should discuss the limitations of the CLGT system in a positive light and indicate how it can be further improved.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}