WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
Authors: Jiahao Wen
Hang Yu
Zhedong Zheng
Venue: NeurIPS 2025 poster ()
Keywords: Training-free
Geolocalization
Text Guidance
Drone Navigation
Primary area: reinforcement_learning

Abstract: Visual geo-localization for drones faces critical degradation under weather perturbations, \eg, rain and fog, where existing methods struggle with two inherent limitations: 1) Heavy reliance on limited weather categories that constrain generalization, and 2) Suboptimal disentanglement of entangled scene-weather features through pseudo weather categories.
We present WeatherPrompt, a multi-modality learning paradigm that establishes weather-invariant representations through fusing the image embedding with the text context. 
Our framework introduces two key contributions: First, a Training-free Weather Reasoning mechanism that employs off-the-shelf large multi-modality models to synthesize multi-weather textual descriptions through human-like reasoning. It improves the scalability to unseen or complex weather, and could reflect different weather strength. 
Second, to better disentangle the scene and weather features, we propose a multi-modality framework with the dynamic gating mechanism driven by the text embedding to adaptively reweight and fuse visual features across modalities. The framework is further optimized by the cross-modal objectives, including image-text contrastive learning and image-text matching, which maps the same scene with different weather conditions closer in the representation space. 
Extensive experiments validate that, under diverse weather conditions, our method achieves competitive recall rates compared to state-of-the-art drone geo-localization methods. Notably, it improves Recall@1 by 13.37\% under night conditions and by 18.69\% under fog and snow conditions. Our code is available at https://github.com/Jahawn-Wen/WeatherPrompt.

WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
Jiahao Wen
Hang Yu- Zhedong Zheng
School of Computer Engineering and Science, Shanghai University, China Facullyy of Science and Technology and Institute of Collaborative Innovation, University of Macau, China / @shu. edu..edu.ccm cn, zhedongzheng@um. edu.mo
yuhang)
Abstract
Visual geo-localization for drones faces critical degradation under weather per- turbations, e.g., rain and fog, where existing methods struggle with two inherent limitations; 1) Heavy reliance on limited weather categories that constrain general- ization, and Suboptimal disentanglement of entangled scene-weather features through pseudo weather categories. We present WeatherPrompt, a multi-modality learning paradigm that establishes weather-invariant representations through fusing the image embedding with the text context. Our framework introduces two key contributions: First, a Training-fres Weather Reasoning mechanism that employs off-the-shelf large multi-modalin models to synthesize multi-weather textual de- scriptions through human-like reasoning. improves the scalability to unseen or complex weather, and could reflect different weather strength. Second, to better dis- entangle the scene and weather features, we propose a multi-modality framework with the dynamic gating mechanism driven by the text embedding to adaptively reweight and fuse visual features across modalities. The framework is further optimized by the cross-modal objectives, including image-text contrastive learning and image-text matching, which maps the same scene with different weather condi- tions closer in the representation space. Extensive experiments validate that, under diverse weather conditions, our method achieves competitive recall rates compared state-of-the-at drone geo-localization methods. Notably, it improves Recall @ by 13.37% under night conditions and by 1 18.69% under fog and snow conditions. Our code is available at https://iihubb.co//laaaaaa WeeeaaawwwwwwwwwWaaaaaee Wen/WeathrrPPooom
Introduction
Drone visual gco-localization aims to match drone-view image with corresponding satellite views, supporting critical applications such as disaster response, urban surveillance, search-and-rescue, and environmental monitoring 2 5 6. However, variable weather conditions such as rain, fog and snow introduce noise, occlusions and low visibility, which severely distort image features (7 7 9) and leading conventional localization methods to suffer drastic performance degradation under extreme weather. Recent advances in cross- moss-modal retrieval show that integrating natural language descriptions can substantially enhance the discrimination power of vision models, allowing better generalization in complex ambiguous scenarios (10 11 12 13 Despite this progress, leveraging textual guidance for cross- wooss-weather drone cooloocalization remains largely underexplored, especially considering the nuanced and dynamic nature of weather conditions encountered in the field. The ability of text to capture complex semantics and fine-grained details [14 [5] offers a promising
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure I: Example of the proposed Chain-of- Thought description and matching, Our framework generates structured weather and spatial Text Description via stepwise reasoning. We leverage Off- the-shelf Visual Grounding Model (VGM), i.e., XVLM [16] extract local region cues, which are integrated to further refine the matching process. Finally, we match images using weather description, global scene layout, and local region semantics to retrieve the corresponding satellite-view image.
avenue for cross-weather generalization. Addressing the all- all-heether visual gco-localization task presents two primary challenges: (1) Limited Weather Labeling. Existing approaches typically perform domain-specific fine-tuning on a limited set of predefined weather labels (e.g., sunny. rainy). This closed-set paradigm fails to capture the continuous and combinatorial nature of real-world weather, thereby limiting model generalization to unseen or mixed conditions and preventing the exploitation of richer weather semantics. (2) Ccene-Weather Feature Entanglement. Existing methods directly inject coarse pscudo-weather labels (e.g., rain, fog) into visual representations during training, leading to a severe entanglement between scene semantics and weather disturbances. Consequently, the model learns suboptimal representations under mixed unseen weather conditions, fails to disentangle scene content from weather noise, and is severely limited in cross weether generalization.
For the first limitation, we propose training-free weather reasoning mechanism leveraging off-the shelf large multimodal models [77] (see Fig. I. Specifically, we employ
(CoT) (18) prompting to automatically generate rich, step-by-step natural language weather descriptions for each geographical scene using a single randomly sampled drone-view image. This strategy circumvents manual description and expert controlled, enabling large-scale collection of high-quality and diverse multimodal samples at significantly reduced costs. Moreover, the stepwise reasoning introduced by the CoT prompts ensures semantic accuracy and formatting consistency across generated captions, further enhancing the reliability and usability of descriptions. This ultimately leads to improved generalization for complex and unseen weather conditions. To address the second challenge, propose a multimodal framework equipped with a text embedding- driven dynamic gating mechanism to adaptively reweight and fuse visual features, effectively disentangling scene and weather attributes. Specifically, during training, the framework jointly optimizes multimodal objectives including image- text contrastive (ITC) loss and image-text matching (ITM) loss, aligning drone images with generated weather-aware captions. Additionally, a localized alignment loss is introduced to explicitly enforce consistency between annotated visual regions and textual descriptions across multiple granularities, encouraging the visual encoder to learn robust and weather-invariant scene representations. At infer- ence, visual and textual embeddings are extracted in parallel, and the textual embeddings dynamically modulate visual features via the gating mechanism. The resulting multimodal representation is directly fed into classification head for localization prediction, avoiding any additional online fine- tuning or dedicated parameter sets for different weather conditions. This design significantly reduces structural complexity and improves the deployment efficiency on resource- constrce-onstrriied platforms. The main contributions are as follows:
Training-Free Weather Reasoning: We pioneer automatic weather semantics extraction through Large Vision Language Models (LVLMs) with of-iin-of-thouhh prompting, eliminating manual descriptions. Our hierarchical reasoning mechanism integrates continuous weather priors with spatial-object analysis, enabling weather-adaptive caption generation and scalable dataset construc- tion.
Semantic Disentanglement via Language Guidance: We devise a text-driven framework achiev- ing disentanglement through: (1) Multi-granularity alignment of visual features with continuous textual weather embeddings, (2) Region-level semantic consistency enforcement, (3) Dynamic textual gating for reather-invarian representations.

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State-of-the-Art Generalization: The proposed method has achieved average 87.72% Recall on University- 1652 and 83.1% on SUS-200 over 10 different weather conditions. For unseen weather combinations (i.e, Dark+Rain+Fog)) ours still arrives at 72%15% AP, validating unprecedented cross- domain generalization.
2 Related Work
Cross-view Geolocalization. Cross-view geo-localization aims to match images captured from
different viewpoints with their corresponding geographic locations [1, 19 20 21 Early approaches relied on hand-crafted local features such as SIFT [22] and SURF [23 as well as global descriptors
like VLAD [24] and Fisher Vector (25). often combined with RANSAC (66] for geometric verification;
however, they remain brittle under large viewpoint and illumination changes (27 28]. With the advent
of deep learning, deep metric learning frameworks based on global or part-based contrastive objectives
have become dominant (9 30 31 32 33 34). These approaches employ triplet or InfoNCE losses
to train end-to-end embeddings and integrate global pooling with spatial attention multi-region partitioning strategies (35 36]. Representative examples include the multi-part partitioning scheme er al. [2], the keypoint attention module (3], the content-aligned Transformer architecture (37), self- attention positional encoding by Yang et al. [35]. the dual-path fusion network (36), and Bird's 's Eye View (BEV) (38). all of which substantially enhance cross-view feature alignment. Recent research has begun to address the impact of image degradations such as low-light, motion blur, and synthetic fog, often by using data augmentation, domain adaptation, or cross-modal transformers [39 40 41
However, most existing methods still depend on a limited set of discrete weather labels, unseen or complex conditions. In contrast, our approach introduces a training-free, all-weather text- text-guided representation learning framework that leverages open-set weather descriptions to overcome these limitations.
restricting generalization
Multi-modality Alignment. In this work, we address weather-aware text-guided representation learning, where the goal is retrieve drone-view images based fine-grat grained weather-related textual cues. Recent advances in vision Assion-language alignment, such as CLIP [45). BLIP [46), and XVLM [16), have established powerful contrastive pre-training and cross-modal attention mechanisms, but previ- ous studies mainly target static semantics or rigid spatial relations [47 48 49). Additional efforts on
adaptive fusion and region-word alignment (50) 5T 52 53 have improved retrieval, but remain lim- ited by closed vocabularies and overlook dynamic, fine-gra grainerin weather semantics. To address these gaps, we propose framework that generates open-set set weather descriptions via Chain-of of-Thought prompting and applies text-driven dynamic gating for adaptive feature modulation, achieving robust cross-modal alignment under diverse weather conditions.
Large Vision Language Models for Vision via Prompting. Large Vision Language Models (LVLMs) such GPT--/44 [54 (55) and Qwen (17) have recently been applied to vision tasks using prompt engineering. Approaches like VisualGIT (56) and MM-CoT (57) leverage Chain-of- Thought (CoT)) prompts (18) elicit stepwise reasoning for visual question answering and captioning. However, scaling LVLMs to cross-view, multi-weather geo-localization remains challenging: free- form text often suffers from hallucinations and lacks semantic structural consistency [58] 59] 60). Prior works also overlook prompt design tailored for robust, muuth--eatherr, multi-scale cross- modal alignment. To address this, introduce the first CoT-driven description pipeline for multi- weather drone-to-satellite geo-localization. Our structured prompts regularize LVLMs' outputs, enabling scalable generation of high-quality, open-set weather descriptions to advance large-scale vision laiiual alignment.
3 Method
3.1
Open- Open-Weather Weather Description
As shown in Fig. we present an overview of our multi-weather drone-view image captioning pipeline. To minimize description redundancy and mitigate overfitting, our approach begins by randomly sampling single representative drone-view image from each geographical region, forming concise yet diverse image-text pairs. Notably, Large Vision Language Models (LVL.Ms) typically generate weather labels or captions intuitively, which may omit critical visual cues and exhibit semantic inconsistency. To address this limitation, explicitly divide the captioning process into
3

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The proposed training-free weather reasoning mechanism. We synthesize drone- view images with diverse weather conditions based on the University-1652 and SUES- 200 datasets, covering complex scenarios such fog, rain, snow, and nighttime. For each synthesized image, we first employ stepwise Chain-of-Thought prompting to generate open-set weather descriptions, including global assessment, local detail analysis, and weather inference. Guided by the inferred weather prior, we then sequentially reason about the scene's 's macro layout, structural elements, and topological relationships, ultimately producing high-quality, structured image-text pairs.
Figure 2:
two sequential phases: weather estimation phase and spatial semantics phase, guided by carefully designed Chain-of- Thought (CoT) prompts. Inspired by human hierarchical visual reasoning, our CoT prompting strategy enforces a rigorous three-step reasoning procedure: global perception, local analysis, and comprehensive synthesis.
In the weather estimation phase, the LVLMs first assesses global visibility quantify observable range, subsequently identifies localized atmospheric indicators such as rain-streak reflections or fog diffusion patterns, and finally integrates these global and local cues to assign accurate weather category. This structured inference process significantly alleviates the ambiguity inherent to single- shot captioning methods, establishing reliable weather-conditionne prior for the following stage.
In the subsequent spatial semantics phase, conditioned explicitly on inferred weather semantic, the model rapidly identifies macro-level layout features, including the spatial distribution of buildings, orientations of roads, and proportion of open spaces. Then, it captures micro-level structural details such object counts, shapes, and local spatial arrangements. Ultimately, the pipeline synthesizes these cues by reasoning about relative positions and topological relationships to generate a structured textual description. This final output aligns comprehensively with weather semantics, spatial structure, and detailed scene attributes, ensuring robust, consistent, and high-quality multimodal descriptions.
Single-image Sampling. Since existing benchmarks (e.g., University-1652) are partitioned by geographic regions, we randomly select only one drone-view image per region as a representative to avoid redundancy. Given the prohibitive cost of obtaining real drone-view imagery across diverse weather conditions, we utilize the imgaug [61] library to synthesize realistic weather variations. By parametrically adjusting visibility and occlusion effects such as rain, fog, and snow, generate high-fidelity, diverse meteorological scenarios that closely resemble real-world conditions.
Weather Estimation Phase. Given drone-vies -iew image with synthetically generated weather, we apply pretrained large multimodal model [I7] for automatic weather description. To mitigate hallucinations, vague phrasing, and inconsistent terminology typical of single-shot captioning, we adopt a Chain-of-TThughhe prompting strategy inspired by human visual perception. Specifically, we first quantify global visibility, then identify local meteorological cues such as rain-streak reflections or fog diffusion, and finally integrate global and local evidence to determine reliable weather label. Any description that lacks explicit visibility information, metcorological cues, or contains

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Figure 3:
uncertain terminology (e.g., "possibly" or "uncertain") is automatically rejected and regenerated. This three-step reasoning pipeline ensures accurate, consistent, and reliable weather descriptions, thus providing robust prior for the subsequent spatial semantics phase.
Spatial Semantics Phase. Conditioned the inferred weather prior, further generate detailed, scene-level textual descriptions essential for precise vision-language alignment. Initially, the model conducts a global scan capture macro-level structures, including building distributions, road orientations, and proportions of open areas. Subsequently, it enumerates fine-grained elements, accurately counting structural entities, describing shapes, and detailing local spatial arrangements. Lastly, it synthesizes macro-layout and micro-level information to explicitly infer relative positions and topological relationships, producing structured, language scene descriptions. These descriptions serve as fine-graines semantic supervision for downstream multimodal alignment.
Discussion. Existing cross cross-view geo-localization methods primarily rely image-based retrieval. While some recent studies [21 62 integrate textual descriptions, these approaches remain limited to coarse, scene-level labels and largely neglect weather semantics, resulting in compromised robustness under diverse weather conditions. In contrast, we introduce a training-free, CoT--driee pipeline for automatically generating multi-weather semantic descriptions. By employing large multimodal ciiii--o--hhooee Thought prompting, we move beyond predefined discrete weather
models guided by Chain-of categories and achieve robust generalization unseen and complex metcorological conditions. Furthermore, our framework systematically annotates both macro-level spatial layouts and micro- level structural attributes, embedding essential spatial cues into the image-text pairs. Relying solely on pretrained multimodal models without manual description, our approach efficiently produces large- scale, semantically consistent multimodal data, significantly facilitating downstream cross- weather multimodal representation learning.
3.2 Multimodal Alignment Model
We introduce a training-free all-weather text-guided representation learning framework (see Fig. that aligns weather semantics injected via text prompts with visual features to produce consistent, discriminative cross-view embeddings across diverse metcorological conditions. The framework consists of a pretrained visual encoder, a text encoder, and a cross-modal fusion module, capped by lightweight classification head. During inference. the text embedding parametrizes gating mechanism that adaptively modulates the visual feature channels; the fused multimodal representation then passed through single-layer MLP to predict the geographic location.
Weather- Driven Channel Gating. Let fi BxD and fT E BxD represent the normalized visual and textual embeddings for batch of B samples. We utilize text embeddings generate adaptive
5

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channel-wise gating vector (0, (0.1)
(f + b(sate)) E BxD//
g =
b2 b(oute)) E (0. 1) BxD
(1)
where W.(oate), W:loate), fused multimodal feature is computed by
by (loate) b2 (lgate)
are learnable parameters, and the reduction ratio. The
fluse = 9 (1 fr E BR
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followed by classification head for aco-localization. By dynamically modulating visual channels conditioned on textual weather semantics, our approach yields discriminative and weather-robust embeddings with minimal computational overhead.
Image-Text Contrastive. Given paired samples {(I, T.) we compute the similarity matrix
Siy = T E IR Rx between normalized visual embeddings and textual embeddings T₁. where T is learnable temperature parameter. We then convert the diagonal entries into retrieval probabilities by applying softmax over rows and columns:
exp(Sat
eSp(Sat Σ 2== exp(S)t)
(8)
PI-T
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Σ 2==1 exp(S
The contrastive loss is defined as:
B
(1) log PogPi-T + log PT-11
Lir = 2B
(4)
enforcing global semantic alignment between the visual and textual modalities.
Image-Text Matching. further enhance fine-grained discriminability, we introduce an image- text matching loss utilizing hard negatives. Given the similarity matrix S, for each image and its paired textual T.). we select the highest diondiigonal similarity entry as its hard negative: T, arg max S.y. 1; arg max Sjt. This forms 2B hard negative pairs (4, T; and T)), and B positive pairs (I; T.), resulting in 3B pairs in total. Each pair is passed through the cross-modal encoder, and we extract the CLS embedding h2 which is fed to binary classifier faachh The matching probability is pk o (faat (ha)). where is the sigmoid function. Denoting the ground- -tuth label ys = for positive pairs and yı: = 0 for negatives, the matching loss is defined as
3B
CITM = 3B
Σ
yb log Pke + (1 y&) log(1 - pt))
(5)
which guides the model to hear subtle semantic distinctions between closely related pairs.
Localized Alignment Module. To enable text-driven fine-grained visual localization, the model responds to both global descriptions T and the region hints Ti, T2, T3. For the j-th concept we extract its [CLS] embedding 4'j Rdd from the cross-encoder, which is projected normalized coordinate vector via a dedicated two-layer MLP:
= σr (W! GELU(W(()))) (loc) xe ((00)) ( E 1]
(6)
(Bloc) E 2d ((lee) Rd., where W-lloe) E Rddxd, Willoc) E 1R2d b (Ce, By, wē h)) encodes the region center and size in normalized coordinates. The localized alignment loss supervises both overlap and regression accuracy:
The resulting vector N
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where = (cz, Cy, w. w. is the ground-truth region, i its prediction, and IoU(...) computes the Intersection-ove--nninn.
Classification Module. After multimodal fusion, we employ a single-layer MI. classification head to predict the geographic location. Let the fused feature for sample b be 26 R and classifier

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Table 1: Performance (R@1(%) and )) on University- 1652 for Drone + Satellite and Satellite - Drone tasks. In both tasks, drone-view images are stylized 10 different weather conditions, and the shellite-view images are constant. Best results are highlighted in bold. denotes the use of official pretrained weights.
output logits og, = Wa + bar E IRC where Werr E CxD and bate E R,C Given the ground-truth location label yb {I...., Cj. the softmax cross-entropy loss is:
B log
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Optimization Objectives. Our total loss Leotal defined as:
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(9)
This unified objective enables the model to learn fine-grained, weather-invariant representations, substantially improving cross-weather generalization.
4 Experiment
Implementation Details. We adopt XVLM [16] as the backbone, which is pre-trained on 4M image-caption pairs, integrates BERT 63 as the text encoder and Swin Transformer [64] as the image encoder. The model is optimized using stochastic gradient descent (SGD) (65 with momentum 0.9 and weight decay 0.0005. Training 210 epochs, with the learning rate reduced by 0.1 epoch 120 and by 0.01 at epoch 180. We resize input images to 384 x 384 pixels and divide them into 32 x 32 patches. During training, satellite-view images are augmented via random cropping and horizontal flipping, for drone-view images, we first apply style transformations using the imgaug [61] library and then perform the same random crop and flip augmentations. At test time, we compute the Euclidean distance between query and candidate embeddings to measure similarity. All experiments have been implemented in PyTorch [66] and conducted on a single NVIDIA RTX A6000 GPU, with an average inference time of 0.024s per query.
Dateset. Un1 is a large-scale cross- -vess geo-localization dataset comprising images from 1,652 university locations. Each location is represented by satellite, drone, and ground-level images, with 54 drone-view and satellite-view images per building, as well as street-view imagery. The dataset is split into 701 training and 951 test buildings, with no overlap between train and test sets. SUES-200 (67) contains vilti-view drone and satellite images from 200 locations in Shanghai, encompassing diverse urban scenes, parks, lakes, and public buildings. Drone images are captured from multiple altitudes (150-300m) to simulate varied real-world conditions.
4.1 Comparison with Competitive Methods
The experimental results on the University-165) dataset are shown in Tab [ We compare the proposed WeatherPrompt with several competitive cross-view geo-localization methods. In the Drone + Satellite task, WeatherPrompt achieves a mean Recall@1 1 (R@) 1) accuracy of 77. 77.14% (+11.99%) and a mean Average Precision (AP) of 80.20% (+11.04%) outperforming existing ofate-of-the-art methods for weatheweather loollizaii..,onn Similarly, in the Satellite - Drone task, WeatherPrompt
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Table 2: Performance (R@ 1(%) ) and AP(%) ()) on SUES-200 for Drone - Satellite and Satellite + Drone tasks. In both tasks, drone-view images are stylized in 10 weather conditions, while satellite-view images remain constant. Best results are highlighted in bold. denotes the use of
official pretrained weights. denotes the use of official pretrained weights on Uni2
Normal
Satellite
Drone
Fog+ Roin
Drone
Satellite
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MuSeNet
Ours
Figure 4: Qualitative comparison under varying weather. While existing methods perform reliably under clear weather, their accuracy drops markedly in adverse conditions. Our approach maintains superior localization performance, especially when drone images are severely affected by weather. Green boxes indicate correct matches, while images in red boxes represent incorrect matches.
attains a mean R@ accuracy of 87.72% (+3.04%) and a mean AP of 76.39% (+ (+10.64%). Our method validates competitive performance when handling drone view images under different conditions, especially in terms of cross-view geo-localization in challenging weather scenarios (e.g.. Dark, Fog+Snow, and Rain+Snow). In addition quantitative comparisons, we present qualitative retrieval results in Figure 4 While existing methods can retrieve plausible matches in clear conditions, they suffer substantial performance drop when encountering adverse weather conditions. contrast, our approach consistently retrieves more accurate matches among the top-ranked candidates even in the presence of severe weather, highlighting its robustness and generalization capability. the real-world captured SUES-200 dataset, we combine drone-view images captured at four altitudes (150m, 200m, 250m, and 300m) to simulate diverse operational heights in practical drone applications, enabling a comprehensive evaluation of our method. As shown in Tab compared to the state-of- of-the-art multi-weather geolocalization method MuSe-Net, our method shows significant improvements in several metrics. Specifically, Ours improves mean R@ 1 accuracy in the Drone + Satellite task from 52.02% to 61.20% (+9. (+9.18%) 18%) and mean AP from 53.33% to 63. 63.26% (+9.80 80%).. In the Satellite - Drone task, our method increases mean R R@1 accuracy from 69.43% to 80.73% (+11.30%) and mean AP from 14% to 66. 12% (+14 (+14.98%).
4.2 Ablation Studies and Further Discussion
Impact of Weather-Driven Gating Mechanism. We conduct ablation experiments to quantify the impact of the weather-driven gating module on multimodal fusion. Table summarizes three alternative fusion strategics under identical settings: (1) Concatenation, which concatenates visual and textual features directly; (2) Static Gate, which fuses modalities using fixed average weights; and (3) Dynamic Gate (Ours), which adaptively reweights visual channels based on weather semantics. We find that the dynamic gating mechanism improves mean AP by 2.3% over the no-gate baseline, with only 0.2M extra parameters and 0.4 ms added latency. This validates that weather-aware whsnnel-wise fusion enables more reliable feature integration, especially under adverse weather. The dynamic gate also reduces degradation from ambiguous weather descriptions, demonstrating its robustness for cross-weather geo-localization.
Impact of Text-Guided Weather Semantics. We conduct an ablation study to assess how multi- weather textual supervision affects cross-view geo-localization. Table 3b reports mean Recall@)
8

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D2S
S2D
Normal
Mean
Normal
Meun
Method
R@ ((%)
AP(%)
R@ ((8)
AP(5)
R@ ((%)
AP(SE)
R 1(%)
AP(%)
Concatenation
81.59
84.11
75.73
78.90
88,59
81.76
85,38
73.84
+Static Gate
82.35
84.75
75.26
78.45
88.45
82.25
85.89
74.61
+Dynamic Gate
82.78
85.18
77.14
80.20
89.16
81.80
87.72
76.39
(a) Impact of Weather- Driven Channel Gating University-16e 1652
5(8)
APX3)
Step
Mran (19)
Mosn AP(R3
Mran AIV
23.22
75.78
NAN
24.53
18:72
S5:17
75.26
MuSe-Ne
31.70
75. 10
19.15
$5.45
75.65
75.35
79.10
$5:85
74.30
Sampleffeuu 21
33.33
61.1
83.33
43.47
75.06
79.80
$6.00
75.20
S2D
MuSe Net 29
38.89
61.11
$3.89
64.34
77:14
80.20
87.72
76.39
Ous
66.66
77.78
94.44
72.15
(b) Different CoT Setting University-1657
(c) Evaluation on Real World Videos
Table 3: Ablation Studies on University-166 and Real-World Videos. D2S denotes the Drone Satellite and S2D denotes the Satellite - Drone.
and mean Average Precision (AP) across two tasks (Drone + Satellite and Satellite Drone) on University-1652, under varying levels of of-hought (CoT) guidance. The no-caption baseline (NAN) removes all text-based components, training the model with only the visual encoder and classification head. When progressively increasing the CoT prompt steps, we observe consistent performance gains for both tasks. Compared the baseline, the best CoT setting (6-step) achieves improvements of +2.61% % mean Recall @1 and +1.48% mean AP in the Drone + Satellite task, and +2.55% mean Recall@) and +3.1 13% mean AP in the Satellite + Drone task. Notably, all models leveraging text-based weather guidance achieve higher mean Recall @ and AP compared to visual-only baseline, demonstrating that even minimal textual supervision provides substantial benefits for model robustness and accuracy.
Impact Prompt Structuring on Multimodal Alignment. To comprehensively assess the impact of stepwise reasoning in prompt engineering on multimodal alignment, we generate captions using the same large vision-language model [17], varying only the number of reasoning steps prescribed in the prompt. The zero-step baseline produces single-turn weather description without explicit (CoT) variants employ multi-stage reason-
intermediate reasoning. contrast, the ing prompts, including 2-step, 4-step, and 6-step configurations. The optimal 6-step CoT scheme decomposes the generation process into two reasoning chains: the first three steps sequentially estimate global visibility, local atmospheric cues, and fine-grained weather semantics; the latter three steps, conditioned on the inferred weather prior, successively infer macro scene layout, enumerate structural elements, and capture fine mne-grained spatial relationships. This structured decomposition compels the model to capture both metcorological variations and spatial semantics, yielding captions that are both semantically rich and consistent in format. As shown in Table 3b model performance steadily improves as the number of reasoning steps increases, with the 6-step CoT achieving the best results in terms of both Recall@ I and mAP. These findings show that fine-grained, multi-stage reasoning significantly enhances cross-weathes weather robustness and discriminative capacity, serving as a erucial ingredient for high-quality dision-languse alignment in challenging multi-weather scenarios.
Real-Wel World Performance under Adverse Weather Conditions. To validate real-world robustness, we collected 54 drone-satellite video pairs from YouTube to evaluate ours under three weather conditions, including Dark, Rain, and Fog. As shown in Tab 3c our model consistently achieves superior results in terms of R @ R @ R@ 10, and Average Precision (AP). These results underscore the strong robustness and generalization capability of our model under challenging real-world scenarios with poor lighting or inclement weather conditions.
Limitations. Despite of significant advancements in cross-view geo-localization under diverse weather conditions, several limitations inherited from external components warrant discussion: (1) Dataset. The evaluation relies on existing datasets, which lack exhaustive geographic and weather diversity. Their limited scope in representing globally rare region-specific weather phenomena may affect generalization unseen environmental extremes. (2) Language Model Biases. The weather and spatial captions generated by off-the-shell vision-languag models inherit biases from

---

their pretraining corpora. Subtle inaccuracies in descriptor granularity, e.g. "haze" vs. "fog", could propagate into the aligmment process. Dataset and LVLM advances will mitigate these limitations.
Conclusion
In this paper, we propose novel training- traiiin-ffree, text-guided multi multi-modality alignment framework for robust cross-view geo-localization under complex and unseen weather conditions. By leveraging large vision-language models, introduce reasoning pipeline that automatically generates high-fidelity weather and spatial captions via mhain-of-thoughh prompting, climinating the need for costly manual descriptions expert-controlled. Our multi-modal alignment model incorporates dynamic channel- wise gating mechanism that adaptively fuses textual weather semantics with visual representations, achieving fine- fine-grained disentanglement of scene and weather features. Extensive experiments the University-1652 and SUES- benchmarks validate that our method consistently outperforms state-of-the-ard approaches, particularly in challenging multi-weather scenarios. Our approach sets new paradigm for leveraging language-driven priors in aerial geo-localization and offers a scalable path toward real-world deployment under diverse environmental conditions.
Acknowledgement
This work is supported by the Shanghai Committee of Science and Technology, China (Grant No.237R 1423500), the National Natural Science Foundation of China under Grant 6230222, University of Macau DYRG-GGRG:022-0007--FTST---- Science and Technology Development Fund (FDCT) 0043/2025/RIAI.
and SRG2024-0000-FSTT, and the
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[72] Jinliang Lin, Zhiming Luo, Dazhen Lin, Shaozi Li, and Zhun Zhong. "A Self-Adaptive Feature Extraction Method for Acrial-View Geo-Localization" In: IEEE Transactions Image Processing (2024).
[73] Haolin Du, Jingfei He, and Yuanqing Zhao. "CCR: A Counterfactual Causal Reasoning- -Based Method for Ctoss-View Geo-Localization" In: IEEE Transactions on Circuits and Systems for Video Technology (2024).
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Reviews:
[{"review_id": "V5vKSDCsLC", "reviewer": "Reviewer_SxCT", "summary": "The paper works on improving geolocalization for drones under diverse weather conditions. The paper argues that there are fundamentally two issues with current methods for drone visual geo-localization: 1) current models take into account pre-defined fixed set of weather labels but real-world weather scenarios are combinatorial and more complex, and 2) the weather and visual representation from existing models are often entangled leading to poor representations on unseen weather situations. To alleviate these issues, the authors first propose a pipeline to generate robust weather prompts by leveraging exisitng pre-trained VLMs. Second, the authors propose a representation learning pipeline that aligns image and text representations and directly predicts the geographic location.", "questions": "1. Please adress some of the comments in the weakness section\n2. This is text from the paper: \"Existing methods directly inject coarse pseudo-weather labels (e.g., rain, fog) into visual representations during training, leading to a severe entanglement between scene semantics and weather disturbances. Consequently, the model learns suboptimal representations under mixed or unseen weather conditions, fails to disentangle scene content from weather noise, and is severely limited in cross-weather generalization.\" Could you please elaborate on why the first sentence leads to the outcome described in the second sentence.", "limitations": "The model is primarily evaluated on the synthetic data generated by authors which makes it difficult to understand the general applicability of the model in real-world scenarios. While there is an experiment on real-world data (Table 3c), the data is very small with limited details, making it unclear if those results will be reproducible in other settings.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n- The motivation for the paper is clear. The sharp degradation in performance of geolocalization models under adverse weather conditions suggest that there is a strong need to design systems that are explicitly conditioned to handle this issue.\n\n## Weaknesses \nMajor:\n- Ln 144 mentions that the authors use \"imgaug\" to generate \"realistic weather variations\". This pipeline needs to be explained and evaluated in detail. Without understanding the design for generating these weather variations, it is hard to judge how good/realistic the synthetic data is. If there is significant difference in the data distribution of the synthetically introduced weather variations, and real weather variations, any downstream model trained on such data will likely overfit to specific patterns in the synthetic data and not generalize well to real-world conditions. Some of the examples shown in figure 2, specially ones with snow, look quite different from a plausibe \"real\" counterpart.\n- There are several components that are missing or atleast confusing, when reading the paper. For example, how inference is done is not clear when reading the paper. The framework itself has a classification module that predicts geolocation, which could be a concievable way to geolocalize. Ln (232-233) mentions that Euclidean distance between query and candidate embeddings is used but it is not clear how the candidate embeddings are generated.\n\nMinor:\n- In Table 2, AP is incorrectly bolded on \"ours\" when Sample4Geo has a higher AP\n- Ln 144: Gramatically incorrect sentence\n- Ln 211: Is it supposed to be MLP?\n- The primary area for this paper is selected as Reinforcement Learning. This paper is much more aligned to Representation Learning than Reinforcement Learning.", "was_revised": true, "final_justification": "The authors have addressed my concerns and I raise my score to borderline accept.", "rebuttal": "**Q1**: The paper states it generates “realistic weather” using imgaug but fails to disclose the synthesis pipeline or realism evaluation, which may cause a synthetic–real distribution gap and overfitting as illustrated by the snow examples in Fig. 2.\n\n**A1**: Thank the reviewer for their attention to realism. \n\n(1) **There is no publicly available real‑world multi‑weather drone dataset, collecting real multi‑weather data is costly and time‑consuming.** Consequently, most prior works have employed parameter‑based synthesis strategies. **We follow prior work that adopt imgaug with continuous random parameter sampling and composable operators.**  If a suitable real‑world multi‑weather drone dataset becomes available, we would be very willing to conduct further validation and report the results.\n\n(2) **We evaluated our method on the SUES-200 dataset, a real‑world dataset that contains significant illumination variations.** As shown in Table 2 on page 7, our method achieves robust performance under various weather conditions.\n\n(3) We used real multi‑weather drone images collected from YouTube as a reference and evaluated them using the Average Intra‑Hash Distance, which ranged from approximately 29 to 31. **The maximum average distance difference between our synthetic images and the real multi‑weather drone images does not exceed 0.96**, indicating that the perceptual variations of the synthetic samples remain within the natural variability of the data.\n\nFor example, our \"Rain” and \"Rain+Snow” augmentation are defined with imgaug as follows:\n\n#\"Rain”\n\niaa.Rain(drop_size=(0.05, 0.1), speed=(0.04, 0.06), seed=38),\n\niaa.Rain(drop_size=(0.05, 0.1), speed=(0.04, 0.06), seed=35),\n\niaa.Rain(drop_size=(0.1, 0.2), speed=(0.04, 0.06), seed=73),\n\niaa.Rain(drop_size=(0.1, 0.2), speed=(0.04, 0.06), seed=93),\n\niaa.Rain(drop_size=(0.05, 0.2), speed=(0.04, 0.06), seed=95),\n\n#\"Rain+Snow” \n\niaa.Snowflakes(flake_size=(0.5, 0.8), speed=(0.007, 0.03), seed=35),\n\niaa.Rain(drop_size=(0.05, 0.1), speed=(0.04, 0.06), seed=35),\n\niaa.Rain(drop_size=(0.1, 0.2), speed=(0.04, 0.06), seed=92),\n\niaa.Rain(drop_size=(0.05, 0.2), speed=(0.04, 0.06), seed=91),\n\niaa.Snowflakes(flake_size=(0.6, 0.9), speed=(0.007, 0.03), seed=74),\n\n**Q2**: The paper does not clearly explain the inference procedure or how the candidate embeddings are generated, even though it mentions using Euclidean distance between query and candidate embeddings for geolocation.\n\n**A2**: We thank the reviewer for highlighting the lack of clarity in the inference procedure. **Pages 2 (Lines 58–60), 5 (Lines 180–184), and 7 (Lines 230–233) describe the inference procedure.** **The generation of candidate embeddings is explained**: the query and each candidate are encoded by the same visual encoder, and the text embedding then parameterizes a gating mechanism to produce the fused representation (Lines 170–178). Figure 3 shows this pipeline.\n\n**During training** , we attach a classification head for geo‑location ID supervision to enlarge inter‑class margins.\n\n**At inference**, the classification head is removed, and we perform retrieval directly on L2-normalized embeddings. In the drone-to-satellite (D2S) setting, the gallery contains only satellite image features (weather-invariant), while each query is obtained by fusing the drone image feature with its chain-of-thought text via a learned gating mechanism, then L2-normalizing the result. In the reverse (satellite-to-drone, S2D) setting, satellite embeddings serve as queries and the gallery is composed of fused drone embeddings. \n\nFinally, we rank by Euclidean distance between normalized vectors (equivalent to cosine similarity) and report Recall@1 and Average Precision (AP).\n\n**Q3**: In Table 2, AP is incorrectly bolded on \"ours\".\n\n**A3**: We thank the reviewer for identifying the formatting error in Table 2. In the revised manuscript, **we have corrected the bolded throughout the table.**\n\n**Q4**: Ln 144: Gramatically incorrect sentence.\n\n**A4**: We thank the reviewer for pointing out the grammatical error at line 144. The sentence has been revised for clarity and correctness: **To address this challenge, we propose the first chain-of-thought–driven description pipeline for multi-weather drone–to–satellite geo-localization.**\n\n**Q5**: Ln 211: Is it supposed to be MLP?\n\n**A5**: We thank the reviewer for pointing out the spelling error at line 211, it should indeed read “MLP.”\n\n**Q6**: The primary area for this paper is selected as Reinforcement Learning. This paper is much more aligned to Representation Learning than Reinforcement Learning.\n\n**A6**: We appreciate the reviewer’s suggestion. **At the time of submission, the system did not offer a “Representation Learning” option.** Because our work addresses drone‑based cross‑view geolocalization and the learned representations serve as initial state estimates for decision making and control (for example, reinforcement learning based planning), we chose “Reinforcement Learning (decision & control, robotics)” as the most appropriate primary area.\n\n**Q7**: Please explain why injecting coarse pseudo‑weather labels directly into visual representations causes severe entanglement of scene semantics and weather noise, thereby limiting the model’s ability to generalize under mixed or unseen weather conditions.\n\n**A7**: We thank the reviewer for this insightful question. **Previous coarse-grained pseudo-weather labels (e.g., simply “rain” or “fog”) provide only category-level information and cannot describe the intensity or duration of a weather event.** In contrast, our sentence-level fine-grained weather descriptions (e.g., “light drizzle blurring the distant horizon, with intermittent heavy showers”) not only specify the weather type but also quantify its severity. We leverage the image-text contrastive loss (ITC) and image-text matching loss (ITM) to guide the network in capturing these subtle weather variations and aligning visual features with their corresponding descriptions. This strategy enables the model to maintain robust retrieval performance under complex or previously unseen weather conditions.\n\n**Q8**: The model is evaluated primarily on synthetic data, and the real‑world experiment is small in scale with limited detail, making it difficult to assess its applicability and reproducibility in real‑world scenarios.\n\n**A8**: We thank the reviewer for the question. **Because no publicly available real‑world multi‑weather drone dataset exists and collecting such data is both costly and time‑consuming, we have to rely on this strategy.** (1) We test on a small set of real multi‑weather drone images collected from YouTube. (2) We apply synthetic weather parameters to publicly available real drone images and evaluate performance. If the reviewer or the community is aware of any suitable open real‑world multi‑weather drone dataset, we would be very willing to conduct further validation and report the results.\n\n**Moreover, we evaluated three weather conditions unseen during training. In both drone to satellite and satellite to drone retrieval tasks, our method consistently outperforms existing approaches in R@1 and AP (Table A).**\n\n| **Method**   | **Task** | **Winter Night R@1 (%)** | **Winter Night AP (%)** | **Freezing Rain R@1 (%)** | **Freezing Rain AP (%)** | **Blizzard R@1 (%)** | **Blizzard AP (%)** |\n|:-------------|:--------:|:-----------------------:|:----------------------:|:-------------------------:|:------------------------:|:--------------------:|:-------------------:|\n| CCR          | S2D      |  69.97                  |  44.08                 |  66.16                    |  55.20                   |  60.88              |  34.23             |\n| SafeNet      | S2D      |  56.92                  |   8.28                 |  41.08                    |   8.15                   |  57.92              |  22.05             |\n| Sample4Geo   | S2D      |  58.35                  |  21.33                 |  65.62                    |  32.76                   |  66.90              |  32.47             |\n| MuseNet      | S2D      |  73.32                  |  40.19                 |  76.03                    |  43.51                   |  69.76              |  35.21             |\n| **Ours**     | S2D      | **82.60**               | **59.43**              | **85.31**                 | **61.17**                | **71.90**           | **41.94**          |\n| CCR          | D2S      |  36.05                  |  38.79                 |  47.29                    |  51.70                   |  25.73              |  30.07             |\n| SafeNet      | D2S      |   3.25                  |   4.87                 |   2.64                    |   4.30                   |  11.46              |  15.43             |\n| Sample4Geo   | D2S      |  25.40                  |  28.60                 |  37.83                    |  41.95                   |  36.00              |  40.42             |\n| MuseNet      | D2S      |  39.45                  |  44.36                 |  44.09                    |  49.01                   |  33.44              |  38.51             |\n| **Ours**     | D2S      | **58.67**               | **63.08**              | **61.52**                 | **65.78**                | **59.76**           | **64.15**          |\n\n*Table A: Results on the University‑1652 dataset under three unseen weather conditions. (S2D: satellite‑to‑drone, D2S: drone‑to‑satellite.)*"}, {"review_id": "giAotHEmT1", "reviewer": "Reviewer_stfx", "summary": "This paper proposes to enhance the models’ robustness under different weather conditions by combining the text information prompted from Large Vision Language Models (LVLMs) and the original image feature at the inference stage. In the proposed “training-free” pipeline, a weather-driven channel gating module, an image-text contrastive loss, and an image-text matching loss are proposed to promote the feature alignment at different granularities. Experiments on two drone-view geo-localization datasets demonstrate the robustness of the proposed pipeline across different weather conditions.", "questions": "See my comments, I could increase my score if these weaknesses can be properly addressed.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Pros:\n- Compared to scene representation using images, text information is inherently robust to the variance of weathers or image degradations. Therefore, it is an interesting and promising idea to leverage the text information to enhance the model’s robustness to different weathers.\n- Experiments on two datasets demonstrate the significant improvement from the proposed method. Also, the results on real world videos show that the proposed method can be appliable to real-world scenarios.\n\nCons:\n- My major concern is about the significance of proposing a so-called \"training-free\" pipeline for different weather conditions, especially for the drone-based geo-localization task. In other words, the model’s robustness can be simply enhanced by training under a random combination of different weather conditions. Since the main results only compare the proposed model with baseline models pre-trained on clean images, it is potentially unfair for these models which have no prior information about weather, and it is hard to see how baselines perform when the model has been exposed to different weather-induced distortions during training.\n- The paper claims generalizability to unseen weather conditions, while the generalization experiments seem to be only performed on different combinations of distortions that have been seen during training. A more comprehensive investigation about the model’s extrapolation ability would further enhance the performance of the model.\n- Although the proposed method can enhance the robustness under various weather conditions, there are instances where performance drops under standard (clean) conditions. This trade-off could be more explicitly analyzed and discussed.\n- The method fuses image features with text embeddings at inference time, but the contribution of textual information to robustness is not clearly analyzed. An ablation study isolating the effect of text-only input would help clarify its role in enhancing robustness under various weather scenarios.", "was_revised": true, "final_justification": "My concerns have been mostly addressed; this paper has some merits in enhancing the robustness of CVGL under weather conditions. I lean towards borderline accept in the final rating.", "rebuttal": "**Q1**: The significance of a “training free” pipeline, arguing that similar robustness could be obtained by training on random weather combinations and that comparing only to baselines trained on clean images without testing them on weather augmented data may be unfair.\n\n**A1**: We thank the reviewer for raising this important question. **Our setting is training free, we do not perform any retraining or finetuning at inference, so the initial baselines were also evaluated without any weather‑specific retraining.** To address the reviewer’s concern, **we then fine-tuning three baseline models (Sample4Geo, Safe‑Net, and CRR) using the exact same multi‑weather random augmentations as ours under university-1652 dataset and report their performance in Table A.** All three baselines exhibit average improvements over their original scores, yet our method still outperforms them by a large margin. On drone‑to‑satellite retrieval, the R@1 and AP gaps remain above 10%; on satellite‑to‑drone retrieval, the gaps exceed 6% and 15%, respectively. These results confirm that our robustness gains are not merely due to exposure to additional augmentations.\n\n| **Method**           | **D2S  mR@1 (%)**  | **D2S mAP (%)**   | **S2D mR@1 (%)**  | **S2D mAP (%)**   |\n|:---------------------|:---------------:|:---------------:|:---------------:|:---------------:|\n| **Sample4Geo (FT)**  |  66.72 (+5.62)  |  70.10 (+5.78)  |  81.63 (+2.81)  |  62.29 (+4.87)  |\n| **Safe‑Net (FT)**    |  62.47 (+1.99)  |  65.99 (+2.63)  |  77.18 (+1.49)  |  59.20 (+0.40)  |\n| **CRR (FT)**         |  68.68 (+6.38)  |  72.09 (+5.22)  |  86.55 (+9.53)  |  66.02 (+2.00)  |\n| **Ours**             |      77.14      |      80.20      |      87.72      |      76.39      |\n\n*TableA: Multi‑weather Fine‑Tuning Results on the University‑1652 Dataset. (D2S: drone‑to‑satellite, S2D: satellite‑to‑drone, mR@1: mean R@1, mAP: mean AP, FT: fine‑tuning, Ours: our paper results. The brackets indicate the results of the increase on the original method.)*\n\n**Q2**: The claim of generalization to unseen weather conditions, noting that experiments only involve distortion combinations encountered during training and calling for a more comprehensive evaluation of the model’s extrapolation ability.\n\n**A2**: We are grateful for the reviewer’s valuable feedback on this matter. **We evaluated three weather conditions unseen during training.** In both drone to satellite and satellite to drone retrieval tasks, our method consistently outperforms existing approaches in R@1 and AP (see Table B). **Moreover, our weather synthesis (for example, Fog+Rain) is based on imgaug with continuous random parameter sampling uniformly drawn from specified ranges**, rather than a fixed combination of Fog and Rain settings. These results show that the gains arise from robust out‑of‑distribution generalization to unseen weather conditions, not merely from exposure to a fixed set of distortions.\n\n| **Method**   | **Task** | **Winter Night R@1 (%)** | **Winter Night AP (%)** | **Freezing Rain R@1 (%)** | **Freezing Rain AP (%)** | **Blizzard R@1 (%)** | **Blizzard AP (%)** |\n|:-------------|:--------:|:-----------------------:|:----------------------:|:-------------------------:|:------------------------:|:--------------------:|:-------------------:|\n| CCR          | S2D      |  69.97                  |  44.08                 |  66.16                    |  55.20                   |  60.88              |  34.23             |\n| SafeNet      | S2D      |  56.92                  |   8.28                 |  41.08                    |   8.15                   |  57.92              |  22.05             |\n| Sample4Geo   | S2D      |  58.35                  |  21.33                 |  65.62                    |  32.76                   |  66.90              |  32.47             |\n| MuseNet      | S2D      |  73.32                  |  40.19                 |  76.03                    |  43.51                   |  69.76              |  35.21             |\n| **Ours**     | S2D      | **82.60**               | **59.43**              | **85.31**                 | **61.17**                | **71.90**           | **41.94**          |\n| CCR          | D2S      |  36.05                  |  38.79                 |  47.29                    |  51.70                   |  25.73              |  30.07             |\n| SafeNet      | D2S      |   3.25                  |   4.87                 |   2.64                    |   4.30                   |  11.46              |  15.43             |\n| Sample4Geo   | D2S      |  25.40                  |  28.60                 |  37.83                    |  41.95                   |  36.00              |  40.42             |\n| MuseNet      | D2S      |  39.45                  |  44.36                 |  44.09                    |  49.01                   |  33.44              |  38.51             |\n| **Ours**     | D2S      | **58.67**               | **63.08**              | **61.52**                 | **65.78**                | **59.76**           | **64.15**          |\n\n*Table B: Results on the University‑1652 dataset under three unseen weather conditions.*\n\n**Q3**: Although the method improves robustness under various weather conditions, it sometimes lowers performance under clean conditions and asks for a more explicit analysis of this trade off.\n\n**A3**: We thank the reviewer for this question. As shown in Table C, compared with normal conditions (trained only on normal), our method’s mean R@1 and AP decrease by at most 1%. This slight performance drop is due to the feature space smoothing regularization introduced by the multi weather augmentation. In contrast, we observe significant improvements under adverse weather scenarios such as Fog, Rain and Snow. Our focus is on achieving robust performance across multiple weather conditions, which aligns better with real world application requirements.\n\n| **Method** | **Task** | **Normal R@1 (%)** | **Normal AP (%)** | **Fog R@1 (%)** | **Fog AP (%)** | **Rain R@1 (%)** | **Rain AP (%)** | **Snow R@1 (%)** | **Snow AP (%)** | **Fog+Rain R@1 (%)** | **Fog+Rain AP (%)** | **Fog+Snow R@1 (%)** | **Fog+Snow AP (%)** | **Rain+Snow R@1 (%)** | **Rain+Snow AP (%)** | **Dark R@1 (%)** | **Dark AP (%)** | **Over-exp R@1 (%)** | **Over-exp AP (%)** | **Wind R@1 (%)** | **Wind AP (%)** | **Mean R@1(%)** | **Mean AP (%)** |\n|:----------:|:--------:|:--------------:|:-------------:|:-----------:|:-----------:|:-------------:|:-------------:|:-------------:|:-------------:|:-----------------:|:---------------:|:-----------------:|:---------------:|:-----------------:|:----------------:|:------------:|:-----------:|:----------------:|:----------------:|:------------:|:------------:|:------------:|:------------:|\n| Clean      | D2S      |     83.54      |     85.82     |    41.48    |    46.77    |     16.00     |     19.94     |     26.49     |     31.68     |       7.23        |       9.71      |       6.44        |       8.85      |       20.34       |       24.59      |     1.30     |     2.32     |       44.22       |       49.55       |     48.07     |     53.34     |     29.51     |     33.26     |\n| **Ours**   | D2S      |     82.78      |     85.18     |    81.46    |    84.03    |     80.34     |     83.11     |     77.60     |     80.67     |      78.75        |      81.69      |      73.38        |      76.94      |       78.41       |       81.40      |    67.22     |    71.06     |       74.20       |       77.63       |     77.26     |     80.27     |     77.14     |     80.20     |\n| Clean      | S2D      |     90.16      |     82.52     |    75.18    |    44.51    |     63.77     |     26.86     |     66.76     |     35.24     |      47.93        |      11.65      |      44.22        |       9.42      |       63.20       |       26.93      |    10.70     |     2.30     |       69.76       |       48.27       |     73.18     |     49.57     |     60.49     |     33.73     |\n| **Ours**   | S2D      |     89.16      |     81.80     |    88.73    |    80.58    |     88.16     |     79.87     |     87.59     |     77.25     |      88.45        |      78.20      |      86.73        |      73.23      |       88.59       |       78.14      |    86.59     |    65.20     |       85.31       |       73.25       |     87.88     |     76.33     |     87.72     |     76.39     |\n\n*Table C: Results on the University‑1652 dataset under no weather conditions training. (Clean: no weather conditions training.)*\n\n**Q4**: The contribution of textual information to robustness is not clearly analyzed.\n\n**A4**: **In Table 3(b) (page 8) and Lines 279–281 (page 9), we isolate the contribution of the text branch by removing both the text encoder and cross‑modal fusion, setting the gating mechanism to identity, and leaving all other settings unchanged.** Under these conditions, D2S exhibits a decrease of 2.61% in mean R@1 and 1.48% in mAP, and S2D shows a comparable decline. Crucially, the performance drop is concentrated in Dark and Fog scenarios, with only a marginal effect under Normal conditions. This demonstrates that the text modality supplies weather‑specific and spatial anchoring cues that bolster discriminability in adverse weather, rather than reflecting a superficial improvement due to increased model capacity or extended training."}, {"review_id": "5j2UE33gh5", "reviewer": "Reviewer_ZGxr", "summary": "This paper proposes a multimodal learning framework named WeatherPrompt, which aims to address the performance degradation of drone-based visual geo-localization under complex weather conditions. This framework establishes weather-invariant representations by fusing image embeddings with textual context to enhance generalization capability across diverse weather scenarios. \nThe main innovations include: 1) A training-free weather reasoning mechanism that automatically generates high-quality weather descriptions using large multimodal models; 2) A text-driven multimodal architecture that disentangles scene and weather features through dynamic gating mechanisms; 3) Comprehensive validation demonstrating superior performance across multiple benchmark datasets.", "questions": "1. On p.2, line 74: Add “@” between “Recall” and “1” (typo correction).\n2. How do the authors plan to further validate the generalization capability of WeatherPrompt on real-world datasets with more diverse and complex weather conditions? Are there any ongoing efforts to collect and test on additional real-world drone geo-localization datasets? Related rating: significance.\n3. Given the use of large pre-trained models like Qwen2.5-VL-32B, what are the computational requirements and potential bottlenecks for deploying WeatherPrompt in real-time applications, especially on resource-constrained platforms? It is important to note that the inference time reported in the paper (0.024 seconds per query) does not include the time required to generate the text descriptions, which is a necessary step for each inference in practical applications. How does this additional time impact the feasibility of real-time deployment, and what strategies can be employed to mitigate this potential bottleneck? Related rating: quality.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. Strengths: The methodology section is comprehensive, with clear explanations of the training-free weather reasoning mechanism and the multi-modality alignment model. The use of chain-of-thought prompting is well-justified, and the reasoning steps are clearly outlined. Besides, the approach leverages the power of large multi-modality models in a novel way, providing a scalable solution for generating high-quality weather descriptions. This could inspire further research on using large language models for vision-language alignment tasks. Thirdly, the paper highlights the importance of disentangling scene and weather features, which is a critical step towards more robust and generalizable geo-localization systems. The proposed techniques could be applied to other computer vision tasks that require weather-invariant representations.\n2. Weaknesses: The paper focuses primarily on drone geo-localization and does not explore the broader applicability of the proposed framework to other vision-language tasks. A discussion on potential extensions or limitations in other domains would strengthen the significance of the work. Besides, the generated weather and spatial descriptions inherit the biases of the pre-trained language model, which may affect the accuracy of the alignment process. Although the model achieves excellent performance on the existing dataset, the limited geographic and weather diversity may hinder its generalization to rare or region-specific weather phenomena on a global scale.", "was_revised": true, "final_justification": "I stand with my rating.", "rebuttal": "**Q1**: The paper does not explore how the proposed framework could extend to other vision‑language tasks.\n\n**A1**: We thank the reviewer for this suggestion. Our current work focuses on the challenge of drone‑based geo‑localization across diverse all‑weather conditions. In future research, we intend to extend the framework to other vision–language applications, such as **all‑weather person re‑identification (Re‑ID) and image–text cross‑modal retrieval**.\n\n**Q2**: Biases in the generated weather and spatial descriptions, along with limited geographic and weather diversity in the dataset, may impair alignment accuracy and generalization to rare or region‑specific weather phenomena.\n\n**A2**: We appreciate the reviewer’s valuable feedback. **Regarding text bias, we agree this is a general risk with large pretrained language models.** Our design uses a fixed chain‑of‑thought prompt to produce structured descriptions restricted to objective physical attributes (weather status, spatial relationship). Moreover, The generated sentence-level weather descriptions are first applied via a gating network to perform channel-wise feature reweighting. The reweighted features are then jointly supervised by three loss functions: the image-text contrastive loss (ITC), the image-text matching loss (ITM), and the geo-ID classification loss. Even if the descriptions occasionally contain erroneous or irrelevant information, these spurious contributions will be suppressed to near zero weights under the combined threefold supervision.\n\nWe evaluated three weather conditions unseen during training. In both drone to satellite and satellite to drone retrieval tasks, our method consistently outperforms existing approaches in R@1 and AP (Table A).\n\n| **Method**   | **Task** | **Winter Night R@1 (%)** | **Winter Night AP (%)** | **Freezing Rain R@1 (%)** | **Freezing Rain AP (%)** | **Blizzard R@1 (%)** | **Blizzard AP (%)** |\n|:-------------|:--------:|:-----------------------:|:----------------------:|:-------------------------:|:------------------------:|:--------------------:|:-------------------:|\n| CCR          | S2D      |  69.97                  |  44.08                 |  66.16                    |  55.20                   |  60.88              |  34.23             |\n| SafeNet      | S2D      |  56.92                  |   8.28                 |  41.08                    |   8.15                   |  57.92              |  22.05             |\n| Sample4Geo   | S2D      |  58.35                  |  21.33                 |  65.62                    |  32.76                   |  66.90              |  32.47             |\n| MuseNet      | S2D      |  73.32                  |  40.19                 |  76.03                    |  43.51                   |  69.76              |  35.21             |\n| **Ours**     | S2D      | **82.60**               | **59.43**              | **85.31**                 | **61.17**                | **71.90**           | **41.94**          |\n| CCR          | D2S      |  36.05                  |  38.79                 |  47.29                    |  51.70                   |  25.73              |  30.07             |\n| SafeNet      | D2S      |   3.25                  |   4.87                 |   2.64                    |   4.30                   |  11.46              |  15.43             |\n| Sample4Geo   | D2S      |  25.40                  |  28.60                 |  37.83                    |  41.95                   |  36.00              |  40.42             |\n| MuseNet      | D2S      |  39.45                  |  44.36                 |  44.09                    |  49.01                   |  33.44              |  38.51             |\n| **Ours**     | D2S      | **58.67**               | **63.08**              | **61.52**                 | **65.78**                | **59.76**           | **64.15**          |\n\n*Table A: Results on the University‑1652 dataset under three unseen weather conditions. (S2D: satellite‑to‑drone, D2S: drone‑to‑satellite.)*\n\nWhile our experiments target common weather conditions, we plan to expand to rarer extreme weather and additional task domains in future work to further assess generalization.\n\n**Q3**: On p.2, line 74: Add “@” between “Recall” and “1” (typo correction).\n\n**A3**: We thank the reviewer for pointing out the typo error, we will correct it in the revision.\n\n**Q4**: How we will further validate WeatherPrompt’s generalization on real‑world datasets with more diverse and complex weather conditions and whether we are collecting or testing on additional real‑world drone geo‑localization datasets.\n\n**A4**: We thank the reviewer for this suggestion. **We replaced the description generator with “GLM‑4.1V‑9B‑Thinking” and retrain training and inference.** The results are consistent with the original setup (no significant differences in R@1/mAP, see Table B), indicating that WeatherPrompt’s gains are not tied to a specific VLLM. We have validated the “train‑once, inference‑free” cross‑weather robustness on existing real‑world datasets (web‑collected) see Table 3(c) on Page 8. We agree that larger‑scale and more complex real‑weather validation is needed. There is no publicly available real‑world multi‑weather drone dataset. We are currently collecting a multi‑weather drone dataset and will release it in the future.\n\n| **Method**           | **D2S  mR@1 (%)**  | **D2S mAP (%)**   | **S2D mR@1 (%)**  | **S2D mAP (%)**   |\n|:---------------------|:---------------:|:---------------:|:---------------:|:---------------:|\n| **GLM**  |  76.52  |  79.85  |  86.62  |  76.33 |\n| **Ours**             |      77.14      |      80.20      |      87.72      |      76.39      |\n\n*Table B: Difference Description Generator Results on the University‑1652 Dataset. (mR@1: mean R@1, mAP: mean AP, GLM: description generator with “GLM‑4.1V‑9B‑Thinking”, Ours: our paper results.)*\n\n**Q5**: The computational requirements and bottlenecks of deploying WeatherPrompt with large pre‑trained models (including text description generation) in real‑time on resource‑constrained platforms and what strategies can mitigate these issues.\n\n**A5**: We thank the reviewer for the insightful question. **On an RTX A6000 with bitsandbytes 4 bit quantization, chain of thought text generation takes about 0.48 s per image and image encoding and retrieval takes about 0.024 s, for a total of approximately 0.504 s per query (about 2 FPS).** The large pre-trained model inference is the primary bottleneck while the vision stack adds negligible overhead. For real time deployment we precompute and cache the gallery’s weather specific text embeddings offline, generate query captions only once at the start of each trajectory or when a weather change is detected and then reuse the cached text, and optionally replace the 32 billion parameter generator with a 7 billion parameter or distilled model to further reduce latency with minimal impact on quality. Importantly our reported results use a single set of pre generated captions at inference and still achieve competitive performance, demonstrating real time feasibility."}, {"review_id": "45q8k4C8fc", "reviewer": "Reviewer_Favf", "summary": "WeatherPrompt introduces a training-free, text-guided multimodal framework for drone geo-localization under diverse weather conditions by leveraging large vision–language models and chain-of-thought prompting to generate structured weather and spatial descriptions. Through a dynamic gating mechanism and contrastive alignment between text and image features, the model achieves robust performance across unseen and adverse weather scenarios, outperforming prior state-of-the-art methods.", "questions": "Please refer to the weaknesses section.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The issue studied in the paper is interesting. It is largely overlooked by existing methods.\n2. The abs and intro sections are well-structured and easy to follow.\n\nWeaknesses:\n1. For Figure 3, how to get the box ( $I$, $I_1$, ... )?\n2. Line 178, the authors claim that their method is training-free, but why is it training-free? There are learnable parameters in the pipeline.\n3. Line 181, it's confused between Figure 3 and the illustration here. The cross-modal fusion module is not reflected in Figure 3.\n4. Lines 185-198 are not clearly written. It is ambiguous whether $T$ refers to the different colored segments in Figure 3 or the entire weather descriptions. Simply referring to \"text embeddings\" is too vague.\n5. For line 193, the samples, What is $C$?\n6. Line 211, MPL should be MLP.\n7. In Table 1, the normal part, the performance drop seems too large. Would training Sample4Geo on the augmented data directly lead to good localization results under different weather conditions?\n8. A very important set of experiments is missing: Why are these four loss functions necessary? Do all of them actually contribute positively? Do they have the same impact under different weather conditions?\n9. Additionally, there is no analysis of hyperparameters. Do the weights of these losses have to be equal?\n10. Why this paper is under \"Reinforcement learning\" primary area?\n11. The code is not open released.", "was_revised": true, "final_justification": "Most of my concerns have been addressed. Therefore, I am willing to raise my score to borderline reject. Other reviewers have given positive feedback, and I hope the author can review the entire text again, as some minor errors were found during the review process that significantly affect the reading experience.", "rebuttal": "**Q1**: For Figure 3, how to get the box？\n\n**A1**: **As indicated in the caption of Fig. 1 (Page 2): \"We leverage Off the-shelf Visual Grounding Model (VGM), i.e., XVLM.\"** Its grounding branch outputs candidate boxes and confidences for each phrase, we take the highest‑confidence box per phrase to draw Fig. 3, with no additional training.\n\n**Q2**: Line 178, the authors claim that their method is training-free, but why is it training-free? There are learnable parameters in the pipeline.\n\n**A2**: \"Training-free\" indicates that the proposed model can handle unseen weather conditions without requiring additional fine-tuning. \nIn contrast to existing methods, which typically necessitate fine-tuning for new weather scenarios, our approach leverages text description inputs to achieve immediate adaptation to novel environments.\n\n**We clarify this around lines 180–187 on Page 5**: the classification head only provides supervision during training and is discarded at inference. **Pages 2 (Lines 60–62) describe the inference procedure: \"avoiding any additional online\nfine-tuning or dedicated parameter sets for different weather conditions.\"**\nAt inference time we simply compute L2‑normalized similarities between query and gallery embeddings, no further training or fine‑tuning is performed, yielding a training‑free pipeline.\n\n**Q3**: Line 181, it's confused between Figure 3 and the illustration here. The cross-modal fusion module is not reflected in Figure 3.\n\n**A3**: **The cross-modal fusion is implemented by the right-hand gray block in Fig. 3 (Self-Attention / Cross-Attention / Feed-Forward).** As is standard practice in cross-modal architectures, image tokens serve as queries while the weather text and spatial phrase tokens provide keys/values. It takes the weather text embedding (top), spatial phrase embeddings (bottom), and image features (center), via cross‑attention it injects textual semantics into the image channels and outputs the fused features used by the ITM / LA / CE losses.\n\n**Q4**: Lines 185-198 are not clearly written. Simply referring to \"text embeddings\" is too vague.\n\n**A4**: “Text embedding” comprises two types: (1) Weather text embedding: the full weather description (top of Fig.3) is fed to the text encoder and the [CLS] token is taken as the global vector $f_t$. (2) Phrase level spatial text embeddings: the lower paragraph is split by color into three phrases (corresponding to regions $I_1/I_2/I_3$ in the figure). Each phrase is fed to the same text encoder to obtain $f_{t1}, f_{t2}, f_{t3}$ (marked with matching colors in Fig.3). In the revision, we will rename “text embedding” to “weather text embedding” and “phrase level spatial text embeddings,” and add corresponding labels in Fig.3 to remove ambiguity.\n\n**Q5**: For line 193, the samples, What is C？\n\n**A5**: C was intended to denote the image description, the correct symbol in the paper should be T. Thank you for pointing out the mistake.\n\n**Q6**: Line 211, MPL should be MLP？\n\n**A6**: Yes, it is MLP. We’ll correct it in the revision. Thank you for pointing out the mistake.\n\n**Q7**: Would training Sample4Geo on the augmented data directly lead to good localization results under different weather conditions?\n\n**A7**: We thank the reviewer for raising this important question. **Our setting is training free, we do not perform any retraining or finetuning at inference, so the initial baselines were also evaluated without any weather‑specific retraining.** We then added results on University‑1652 dataset where methods are trained with the same multi‑weather random augmentations as ours. To address the reviewer’s concern, **we then fine-tuning three baseline models (Sample4Geo, Safe‑Net, and CRR) using the exact same multi‑weather random augmentations as ours and report their performance in Table A.** All three baselines exhibit average improvements over their original scores, yet our method still outperforms them by a large margin. On drone‑to‑satellite retrieval, the R@1 and AP gaps remain above 10%; on satellite‑to‑drone retrieval, the gaps exceed 6% and 15%, respectively. These results confirm that our robustness gains are not merely due to exposure to additional augmentations.\n\n| **Method**           | **D2S  mR@1 (%)**  | **D2S mAP (%)**   | **S2D mR@1 (%)**  | **S2D mAP (%)**   |\n|:---------------------|:---------------:|:---------------:|:---------------:|:---------------:|\n| **Sample4Geo (FT)**  |  66.72 (+5.62)  |  70.10 (+5.78)  |  81.63 (+2.81)  |  62.29 (+4.87)  |\n| **Safe‑Net (FT)**    |  62.47 (+1.99)  |  65.99 (+2.63)  |  77.18 (+1.49)  |  59.20 (+0.40)  |\n| **CRR (FT)**         |  68.68 (+6.38)  |  72.09 (+5.22)  |  86.55 (+9.53)  |  66.02 (+2.00)  |\n| **Ours**             |      77.14      |      80.20      |      87.72      |      76.39      |\n\n*TableA: Multi‑weather Fine‑Tuning Results on the University‑1652 Dataset. (D2S: drone‑to‑satellite, S2D: satellite‑to‑drone, mR@1: mean R@1, mAP: mean AP, FT: fine‑tuning, Ours: our paper results. The brackets indicate the results of the increase on the original method.)*\n\n**Q8**: Why are these four loss functions necessary? Do all of them actually contribute positively? Do they have the same impact under different weather conditions?\n\n**A8**: **We follow common practice in vision language retrieval and set the weights of ITC and ITM to 1 : 1**, prior work such as ALBEF (Li et al., 2021), BLIP (Li et al., 2022), FLAVA (Singh et al., 2022), SIMLA (Khan et al., 2022), XVLM (Zeng et al., 2021) and more have also adopted equal weighting of the image‑text contrastive and matching losses, demonstrating stable performance. \n\n**Following prior work, we keep ITC and ITM as our standard objectives for cross modal retrieval and ablate the other two losses**, LA and CE. Table B shows that both losses contribute positively: CE provides a clearer global margin, while LA yields larger gains under low visibility conditions (Dark and Fog) and only minor changes under Normal. In summary, ITC and ITM enable cross modal alignment, CE enforces inter class discrimination, and LA stabilizes local semantic alignment, and they form a complementary set of objectives.\n\n| **Method**           | **D2S  mR@1 (%)**  | **D2S mAP (%)**   | **S2D mR@1 (%)**  | **S2D mAP (%)**   |\n|:---------------------|:---------------:|:---------------:|:---------------:|:---------------:|\n| **W/O LA**  |  76.57  |  79.70  |  86.58  |  75.53  |\n| **W/O CE**    |  75.31  |  78.55  |  86.44  |  75.40  |\n| **Ours**             |      77.14      |      80.20      |      87.72      |      76.39      |\n\n*Table B: Ablation Study of Loss Necessary Results on the University‑1652 Dataset. (W/O LA: without LA loss, W/O CE: without CE loss.)*\n\n**Q9**: Do the weights of these losses have to be equal?\n\n**A9**: **We follow common practice in vision and language retrieval and set the weights of ITC and ITM to 1:1; prior work such as CLIP, ALIGN and more, also adopts equal weighting with stable performance.** Localized Alignment (LA) loss acts as a training time localization consistency regularize and is included in the total loss to stabilize phrase–region alignment and shape the embedding space. **At inference, the retrieval score is computed only from the similarity between L2 normalized image and text embeddings, the scoring function does not include LA.** In implementation, we normalize the box error and average over boxes so that its gradient magnitude is comparable to ITC and ITM, hence no separate reweighting is required. \n\nTo validate our weighting scheme, **we conducted two ablation studies: (1) 2·(ITC+ITM) : 1·LA : 1·CE and (2) 1·(ITC+ITM) : 1·LA : 2·CE.** As shown in Table C, the equal weight configuration (Ours) is best on both D2S and S2D; moderately increasing the contrastive term causes small fluctuations, while increasing CE significantly reduces R@1 and AP. Specifically, relative to Ours, 2·(ITC+ITM) lowers D2S R@1 and AP by 1.22% and 0.95%, and lowers S2D R@1 and AP by 0.43% and 0.33%. In 2·CE settings lowers D2S R@1 and AP by 5.84% and 5.91%, and lowers S2D R@1 and AP by 3.57% and 4.64%. These results indicate that equal ITC and ITM with balanced LA and CE is a robust choice.\n\n| **Method**           | **D2S  mR@1 (%)**  | **D2S mAP (%)**   | **S2D mR@1 (%)**  | **S2D mAP (%)**   |\n|:---------------------|:---------------:|:---------------:|:---------------:|:---------------:|\n| **2(ITC+ITM)**  |  75.92  |  79.25  |  87.29  |  76.06  |\n| **2CE**    |  71.30  |  74.29  |  84.15  |  71.75  |\n| **Ours**             |      77.14      |      80.20      |      87.72      |      76.39      |\n\n*Table C: Difference Loss Weighting Scheme Results on the University‑1652 Dataset.*\n\n**Q10**: Why this paper is under \"Reinforcement learning\" primary area?\n\n**A10**: At submission time, the system did not offer a “Representation Learning” option. Given that our task is drone (robotics) cross‑view geolocalization and the learned representation directly serves as a precursor state estimate for decision and control (RL/planning), we selected Reinforcement Learning (decision & control, robotics) as the closest area.\n\n**Q11**: The code is not open released.\n\n**A11**: We will open source the entire code shortly, including trained weights and the collected real world dataset."}, {"review_id": "SsrUsZOsJ8", "reviewer": "Reviewer_BCVv", "summary": "This paper proposes WeatherPrompt, a novel multi-modal learning framework for robust drone visual geo-localization under diverse weather conditions. The key innovation lies in leveraging CoT prompting with LVLMs to generate structured weather-aware textual descriptions without manual labeling, enabling scalable training data creation. The framework further introduces a text-driven dynamic gating mechanism to disentangle scene and weather features adaptively, enhanced by multi-modal contrastive and matching losses. Experiments on University-1652 and SUES-200 datasets demonstrate significant improvements over state-of-the-art methods, particularly in challenging conditions (e.g., +18.69% Recall@1 for fog/snow).", "questions": "(1) The article points out that large vision-language models usually generate weather labels or descriptions in an intuitive manner. However, how can you ensure that the descriptions guided by CoT prompts are free from factual biases?\nThe current approach only improves the accuracy of the descriptions provided by LLMs, but it cannot supervise whether these descriptions are accurate.\n\n(2) Expansion of (1): The 6-step CoT prompt is central to description quality. Was this structure empirically optimized? A comparison with simpler prompts (e.g., direct QA) would help justify the complexity.\n\n(3)Regarding the second challenge in the intro: (2) Scene–Weather Feature Entanglement is somewhat ambiguous.\nThe entanglement of scene and weather features here is not clearly explained. Both the text description and the image features should contain scene and weather information. The disentanglement here should be targeted at the scene and weather in the two modalities.\n\n(4) You report strong performance on Dark+Rain+Fog, but the dataset’s synthetic weather may not capture real-world complexity. How does the model handle natural weather transitions without explicit CoT supervision during inference?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The method is intuitive. Currently, it is a trend to use large language models (LLMs) for scene description. However, direct image description by LLMs may lead to hallucination, while using chain-of-thought (CoT) can guide the output of LLMs to meet expectations.\n\n2. The training-free description pipeline generalizes to unseen or mixed weather conditions, addressing a key limitation of closed-set methods. Currently, many methods utilize learning from synthetic data to generalize to the real world.\n\n3. Comprehensive benchmarks across 10+ weather types and ablation studies validate the design choices\n\nWeaknesses:\n\n1. The method is intuitive but overly simplistic. Regarding the proposed scene description method, although it is training-free, its corresponding contribution points are much fewer. Moreover, the multimodal alignment framework also lacks key innovative points.\n\n2. I still have some questions to ask you, all of which are included in Questions.", "was_revised": true, "final_justification": "The author has solved all my problems, thus I have decided to raise my rating.", "rebuttal": "**Q1**: The method intuitive yet overly simplistic, with limited contributions from the training‑free scene description and insufficient innovation in the multimodal alignment framework.\n\n**A1**: We thank the reviewer for the feedback. **Although our method is simple, it is effective.** It achieves the highest combined mean R@1 and AP across all weather conditions, significantly outperforming existing baselines and demonstrating its efficacy (see Tables 1 and 2 in paper). **Our main contribution is to address the unresolved challenge of generalizing to unseen/mixed weather, proposing the new “train once, inference without finetuning” all‑weather cross‑view geolocalization framework.**  \n\n**Previous coarse-grained pseudo-weather labels (e.g., simply “Rain” or “Fog”) provide only category-level information and cannot describe the intensity or duration of a weather event.** By leveraging fine‑grained description along with image‑text contrastive (ITC) and matching (ITM) losses, we achieve efficient multimodal alignment that balances simplicity with practical deployment needs.\n\n**Q2**: How to ensure that CoT prompt guided weather descriptions are factually unbiased and accurate, given that the current approach cannot verify their correctness.\n\n**A2**: **We randomly sampled instances and audited the factuality of the CoT weather texts using an independent LLM judge (Llama) and three cs PhD. The accuracy of the weather terms is about 70%, with high agreement between human and model scores.** Most errors arise from boundary/compound states (e.g., “Fog” or “Fog+Snow/Rain”), where continuous intensities and mixtures are hard to express with discrete words. We use a fixed‑template chain‑of‑thought prompt that outputs only objective physical attributes (brightness, visibility, shadows, and major structural orientations), minimizing the introduction of subjective or biased tokens. Because these descriptions serve solely for channel‑level feature re‑weighting, any occasional biased or irrelevant tokens see their weights driven toward zero under our joint contrastive and classification supervision. To demonstrate our method’s independence from a specific LLM, we replaced the caption generator with “GLM‑4.1V‑9B‑Thinking”, retrained and retested, R@1 and mAP remained consistent (see Table A). We also validated cross‑weather robustness on existing web‑collected real‑world multi‑weather datasets (see Table 3(c), Page 8). We agree on the need for larger‑scale, complex real‑weather evaluations. There is no publicly available real‑world multi‑weather drone dataset, collecting real multi‑weather data is costly and time‑consuming. So we are actively collecting a multi‑weather drone dataset for future release.\n\n| **Method**           | **D2S  mR@1 (%)**  | **D2S mAP (%)**   | **S2D mR@1 (%)**  | **S2D mAP (%)**   |\n|:---------------------|:---------------:|:---------------:|:---------------:|:---------------:|\n| **GLM**  |  76.52  |  79.85  |  86.62  |  76.33 |\n| **Ours**             |      77.14      |      80.20      |      87.72      |      76.39      |\n\n*Table A: Difference Description Generator Results on the University‑1652 Dataset. (D2S: drone‑to‑satellite, S2D: satellite‑to‑drone, mR@1: mean R@1, mAP: mean AP, GLM: description generator with “GLM‑4.1V‑9B‑Thinking”, Ours: our paper results.)*\n\n**Q3**: Expansion of (1): The 6-step CoT prompt is central to description quality. Was this structure empirically optimized? A comparison with simpler prompts (e.g., direct QA) would help justify the complexity.\n\n**A3**: **The structure has been empirically optimized.** We evaluated the 6‑step CoT design (L276–L287, Table 3b, Page8): a single‑sentence prompt yields only modest gains over the Image‑only variant, and simplified CoT versions (2/4 steps) underperform the 6‑step design.\n\n**Q4**: The description of scene–weather feature entanglement ambiguous and asks how disentanglement specifically separates scene and weather information across both image and text modalities.\n\n**A4**: We thank the reviewer for this insightful question. Unlike prior works that inject coarse tags like rain or fog directly, we assign each image a fine-grained sentence-level weather description. We apply the image‑text contrastive loss (ITC) and the image‑text matching loss (ITM) to guide the network in capturing subtle weather variations within its feature representations. For example, conventional methods tag all precipitation as rain treating drizzle and heavy downpour as the same class. Our chain of thought prompt generates detailed weather descriptions such as drizzle showers heavy rain or hail so that the network can adjust feature weights based on specific weather information instead of relying on a single rain tag resulting in more accurate representation of weather cues.\n\n**Q5**: How the model manages natural weather transitions during inference without explicit CoT supervision given that synthetic weather may not capture real world complexity.\n \n**A5**: The synthetic “Dark + Rain + Fog” augmentation is not produced by overlaying three fixed labels. Instead, **imgaug continuously samples augmentation parameters at random, generating a vast number of different combinations during training effectively scattering points throughout the parameter space.** Real video phenomena such as gradual darkening, intensifying fog, and variable rainfall correspond to specific values or transitions between values in this space. Because the sampling is dense, the states of real frames are “covered” by adjacent training samples, enabling the model to generalize to natural intermediate weather conditions. At inference, we modulate features using pre‑generated CoT descriptions that encode both weather and spatial information. As a result, when weather changes slowly or transitions smoothly in natural environments, our model maintains high robustness without requiring frequent regeneration of descriptions. **Moreover, we evaluated three weather conditions unseen during training. In both drone to satellite and satellite to drone retrieval tasks, our method consistently outperforms existing approaches in R@1 and AP (Table B).**\n\n| **Method**   | **Task** | **Winter Night R@1 (%)** | **Winter Night AP (%)** | **Freezing Rain R@1 (%)** | **Freezing Rain AP (%)** | **Blizzard R@1 (%)** | **Blizzard AP (%)** |\n|:-------------|:--------:|:-----------------------:|:----------------------:|:-------------------------:|:------------------------:|:--------------------:|:-------------------:|\n| CCR          | S2D      |  69.97                  |  44.08                 |  66.16                    |  55.20                   |  60.88              |  34.23             |\n| SafeNet      | S2D      |  56.92                  |   8.28                 |  41.08                    |   8.15                   |  57.92              |  22.05             |\n| Sample4Geo   | S2D      |  58.35                  |  21.33                 |  65.62                    |  32.76                   |  66.90              |  32.47             |\n| MuseNet      | S2D      |  73.32                  |  40.19                 |  76.03                    |  43.51                   |  69.76              |  35.21             |\n| **Ours**     | S2D      | **82.60**               | **59.43**              | **85.31**                 | **61.17**                | **71.90**           | **41.94**          |\n| CCR          | D2S      |  36.05                  |  38.79                 |  47.29                    |  51.70                   |  25.73              |  30.07             |\n| SafeNet      | D2S      |   3.25                  |   4.87                 |   2.64                    |   4.30                   |  11.46              |  15.43             |\n| Sample4Geo   | D2S      |  25.40                  |  28.60                 |  37.83                    |  41.95                   |  36.00              |  40.42             |\n| MuseNet      | D2S      |  39.45                  |  44.36                 |  44.09                    |  49.01                   |  33.44              |  38.51             |\n| **Ours**     | D2S      | **58.67**               | **63.08**              | **61.52**                 | **65.78**                | **59.76**           | **64.15**          |\n\n*Table B: Results on the University‑1652 dataset under three unseen weather conditions.*"}]

Author rebuttal:

Meta-review: The paper was reviewed by 5 experts in the field. After the rebuttal and discussion, most reviewers gave positive ratings. The remaining concerns from Reviewer Favf are primarily related to the clarity of the paper. The AC reads the paper, the reviews, and the authors' responses carefully and agrees with the reviewers' consensus. The authors are encouraged to make the necessary changes to the best of their ability to incorporate reviewers' suggestions and may consider improving the clarity of their paper.
Decision: Accept (poster)