Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
Authors: Chen Li
Huiying Xu
Changxin Gao
Zeyu Wang
Yun Liu
Xinzhong Zhu
Venue: NeurIPS 2025 poster ()
Keywords: Object detection
Single Domain Generalized Object Detection
Robustness Object Detection
Primary area: applications
TL;DR: This paper explores using causal visual prompts to address spurious correlations in single-domain generalization

Abstract: Single-source Domain Generalized Object Detection (SDGOD), as a cutting-edge research topic in computer vision, aims to enhance model generalization capability in unseen target domains through single-source domain training. Current mainstream approaches attempt to mitigate domain discrepancies via data augmentation techniques. However, due to domain shift and limited domain‑specific knowledge, models tend to fall into the pitfall of spurious correlations. This manifests as the model's over-reliance on simplistic classification features (e.g., color) rather than essential domain-invariant representations like object contours. To address this critical challenge, we propose the Cauvis (Causal Visual Prompts) method. First, we introduce a Cross-Attention Prompts module that mitigates bias from spurious features by integrating visual prompts with cross-attention. To address the inadequate domain knowledge coverage and spurious feature entanglement in visual prompts for single-domain generalization, we propose a dual-branch adapter that disentangles causal-spurious features while achieving domain adaptation via high-frequency feature extraction. Cauvis achieves state-of-the-art performance with 15.9–31.4\% gains over existing domain generalization methods on SDGOD datasets, while exhibiting significant robustness advantages in complex interference environments.

Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
Chen Li If Huiying Xu Changxin Gao!- Zeyu Wang
Yun Liu
Xinzhong Zhu
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Northwest Polytechnical University, Nankai University. (lichenrui27, cgao) .eutttedd.coo cn
Abstract
Single-souree Domain Generalized Object Detection (SDGOD), outting-edge research topic in computer vision, aims to enhance model generalization capability in unseen target domains through single-source domain training. Current main- stream approaches attempt to mitigate domain discrepancies via data augmentation techniques. However, due to domain shift and limited domain-specific knowledge, models tend to fall into the pitfall of spurious correlations. This manifests as simplistic classification features (e.g., color) rather
the model's over-reliance than essential domain-invarian representations like object contours. To address this critical challenge, we propose the Cauvis (Causal Visual Prompts) method. First, we introduce a Cross-AAtention Prompts module that mitigates bias from spurious features by integrating visual prompts with cross- atsss-tttntion. To address the inadequate domain knowledge coverage and spurious feature entanglement in visual prompts for single-domain generalization, we propose a dual-branc adapter that disentangles causal-spurious features while achieving domain adaptation via high-frequency feature extraction. Cauvis achieves state-of-the of-the-art performance with 15.9-31.4%9 gains over existing domain generalization methods on SDGOD datasets, while exhibiting significant robustness advantages in complex interference environments.
Introduction
Autonomous driving object detection models face challenges from complex targets and variable weather conditions, where domain shifts significantly degrade detection accuracy. Compared open- set [ or few-shot object detection [2]. Single-domain Generalized Object Detection (SDGOD) [3] which relies solely on single-source domain data for training without cross- cross-domain prior knowledge (neither multi-domain training data nor target domain prompts) - exhibits severe overfitting when encountering domain shifts. This has become a critical bottleneck restricting the practical deployment of autonomous driving perception systems.
The core challenge of SDGOD lies in achieving precise multi-object localization and recognition under multi-domain scenarios. Machine learning models in such tasks are prone to training data bias, over-relying spurious correlations between target labels and background noise/secondary features. These spurious correlations arise through two interconnected mechanisms: spatially, as training data predominantly position vehicles in central image regions (characteristic of main lane scenarios in Cityscapes [4) and pedestrians near edges (typical sidewalk areas), models erroncously establish
1 Equal contributions. "Correpondiig Author. Project Link: httpss//giitabb..mm//ccheeell http://hthhab.cmmmm/cccee11////0
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Source Domain
w/o Visual Prompts
Spurious Features (Color)
are white.
x Caused by spurious features
Target Domain
x Tends to predict white trucks.
Ours
Causal Features
x Judgment Error
Dual-Branch Adapter
bus: 38.72
Frozen
Atiention
Cross
Aigts
Causal Auxiliary
High
Prompts
Tunable
Branch Branch Frequency
Mitigating spurious correlation via Causal Visual Prompts.
Cross-Attention Prompts
The interference of spurious features (such color) between the source domain and the target domain causes existing methods (7) be prone to erros in the target domain, often mistakenly identifying the color white as trucks, showing poor generalization ability. Our method, leveraging Causal Visual Prompts combined with branch that can extract high-frequency features, effectively stops the model from making predictions based on spurious features, improving its accuracy in the target domain. "Ice" signifies frozen parameters, while "fire" indicates fine-tunable parameters.
Figure 1:
"vehicle-center" spatial bindings while rigidly anchoring pedestrians to peripheral zones, thus ignoring natural variations in real-world object distributions. parallel, color biases emerge through misguided color-category associations, specifically linking white hues to buses/trucks (common in BDD100K urban transit liveries [5]) and black objects to cars. Crucially, such spurious correlations based on superficial features lead to significant degradation in the model's generalization performance in cut-of-Distribution (OOD) scenarios. Although recent work like UFR [6] attempts to address scene confounders through causal attention learning, their reliance on Faster R-CNN 7 frameworks and heuristic perturbation strategies limits the theoretical grounding in disentangling causal features.
While existing studies leverage Domain-Invarian Representation theory 3 to enhance OOD general- ization, the systematic analysis spurious correlations remains insufficiently addressed. Current primarily pursue two complementary strategies: domain-invariant con- straints that architecturally disentangle invariant features from domain-specific characteristies [6], and data augmentation approaches B 9 which simulate diverse domain distributions suppress biased feature dependencies through extended feature space coverage. Nevertheless, these empirically-de solutions lack unified thcoretical grounding, often attributing model deficiencies to symptomatic biases (data skews/attention swsaallccaaiinsppoottyya distortions) rather than addressing the funda- mental issue the inherent prevalence of spurious correlations within single-domain training data.
methodologies 3 6 & (]]
To address these issues, we propose Cauvis (Causal Visual Prompts) method whose core innovations include: Cross-Attention Prompts that mathematically establish the equivalence between visual prompts and backdoor adjustment operations (10] in causal inference, providing theoretical foun- dations for spurious correlation mitigation; and designing a Dual-Branch Adapter that disentangles causal/spurious features via systematic representation decoupling. while enhancing cross-domain knowledge coverage. Specifically, it integrates Fourier transform to achieve causal feature decou- pling through fighfrreueecc component extraction, while incorporating nonlinear feature modeling modules to enhance the prompt's 's domain distribution coverage capabilities.
Our principal contributions are:
We are the first to introduce DINOv2 II as the backbone in the SDGOD. By freezing parameters to reduce training costs, extensive experiments validate that this approach significantly enhances detector performance.

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Through theoretical analysis and experimental observations, we connect spurious correla- tions to the decline in generalization performance, establishing a new theoretical framework for SDGOD.
We propose Cross Attention Prompts with theoretical analysis demonstrating their equiv- alence to backdoor adjustment mechanisms for suppressing spurious correlations. Fur- thermore, we design a Dual-branch Adapter that addresses the limitations of conventional methods in domain knowledge coverage and feature decoupling through explicit causal relationship modeling.
Related Work
Single Domain Generalized Object Detection. In the field of SGDOD, existing approaches pri- marily focus on two strategies: 1) imposing domain-specific constraints to extract domain-invariant representations, and 2) enhancing input diversity through systematic data augmentation. CDSD B
introduces cyclie-disentanglee self seff-distillation framework to decouple domain-invariant represen- tations through multi-stage loss constraints. OA-DG [12] employs multi-level transformations and object-aware mixing strategies to reduce inter-domain representational discrepancies. SRCD 9 constructs self-augni eeff-augmented compound domains to mitigate salegory-backgroon
bias. UFR 6 lever-
data augmentation to simulate potential domain distributions. ClipGap [I3] utilizes cross- poss--mddal prompting from the CLIP model [14] to achieve semantics guided enhancement. G-NAS [8] optimizes domain- invariant constraints via differentiable neural architecture search. Our work achieves efficient generalization without introducing artificially synthesized training samples.
ages
Visual Prompts. To mitigate cross-domain discrepancies, visual prompting (VP) [55, inspired by prompt learning in NLP, has emerged as efficient adaptation paradigm. It introduces lightweight tunable parameters, typically optimizing only 1% of model weights, to align with downstream tasks. In few-shot tasks, VP [6]] enhances model adaptability to novel categories via sparsely annotated perturbation mechanisms like spatial attention-guided local enhancement. Visual Prompt Tuning (VPT) [17, a representative approach, embeds learnable prompt vectors across Transformer layers to enable hierarchical feature interaction for parameter udaptation. Rein [18), an efficient parameter tuning method, is essentially a unique form of visual prompt. These conventional VP methods [17]- 19 couple prompt features with frozen backbones without explicit causal/spurious disentanglement mechanisms.
Our approach fundamentally diverges from NLP prompting mechanisms: While NLP utilizes discrete semantic operations (e.g.. "MASK" token prediction) for textual reconstruction, visual prompts operate in continuous pixel space through frequency-domain perturbations or adversarial noise injection activate cross-domain knowledge in pretrained VFMs.
3 Motivation
3.1 Experimental Demonstration of Spurious Correlations
models often suffer from degraded OOD generalization due to over-reliance on spurious correlations in training data. This issue is particularly critical in dense detection tasks: when models focus non-causal domain-specine color) that coincidentally correlate with labels, rather than intrinsic causal features of objects, their performance significantly deteriorates in unseen domains.
In image classification tasks 20 22
features (e.g., background or
To clearly illustrate this issue, we select the bus and truck categories as representative samples for analysis. These categories often have the same color across domains, appearing predominantly white. (see Fig. D. To create a controlled experimental environment, we crop images of these cate- gories to task-related areas and filter out images with numerous other detection objects, simplifying experimental conditions.
In the experimental design, we artificially increase the probability p: of the color-category association (i.e, raising the probability of white color the truck category to p:) and ensure the non-white distribution of the other category (bus) is ps- Based on varying bias strengths, we create several datasets with different bias levels (p: E [0.75, 0.8, 0.85, 0.9)) and use the original test data as an unbiased test. We train and test on four biased datasets and then retest the results the unbiased

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Table 1: Comparison of DINO
23 and with prompts on SDGOD and CD-FSOD
Backbone
Mothod
Zero Shor (SDGOD)
Shot (CD-FSOD)
DC
DF
DR
NR
NC
1-Shot
5-Shot
10-Shot
DINOV2 M
DINO 23
66.9
50.5
57.0
42.1
54.8
16.3
32.6
34.3
(Large)
Prompis
67.4 +0.5
51.7++12
57.3
41.00-11
57,41+2.6
20.47+4.1
+3.9
37,11+2.7
DINOv2 Heatmap
Cauvis Heatmap
Cauvis & DINOv2 differences
-0.2
blaed Bua
blaed
and Tlack
Test the biased datasets.
DINOv2
Cauvis
Origin Image
DRNON2
Counts
DRMDx2
Couets
(b)
outside the objocts.
Figure 2: Left: Visualization results of DINOv2 II and Cauvis, along with their heatmaps and the differences between. Right: (a) performance of Bus and Truck the biased and unbiased test datasets. (b) Cauvis exhibits stronger activations both within object's interior and outside region.
dataset. As shown in Figure the model's dependence on color features evolves. When p: rises from 0.75 to 0.9, experimental accuracy for trucks rises by 4.2%, but the Truck mAP on the unbiased test set drops by 21%, indicating stronger model reliance on color shortcuts.
Notably, the model achieves higher mAP on biased test sets as increases, suggesting its growing reliance simplistic color-category correlations for decision-making. Our control experiments show that detection models may overly depend on color or background correlations, especially when training data has systematic bias. While comprehensive quantification of this issue is challenging, these observations offer crucial insights and reveal clues previously overlooked in research.
Visual prompts provide semantic constraints beyond pixels. By describing objects' essential attributes, like their geometry or structured volume, they guide models to form domain-invariian causal representations. This helps models avoid relying statistical shorteuts in training data.
3.2 Visual Prompts and Causal Representation
From the perspective of causal representation learning, we can decompose input feature representa- tions into causal fo (z:) and spurious parts (z). The goal is to make the model sensitive fa(x) but robust to or capable of ignoring fs()). ). common strategy is to introduce interventions by ap- plying perturbations 8 ~ p(A) to the inputs, simulating observations in different environments for instance, by artificially altering the background or adding noise (a' = r + 5), thus forcing the model to focus on stable features fø
22 25).
Existing work 24 shows that if model can only use purely causal information (e.g., representations of foreground regions) for prediction, this is equivalent to performing causal intervention on the system, which can greatly boost performance under out-of-diittribution (OOD) scenarios. For example, the CFA 24 does this by training only on image representations containing foreground (causal features), equivalent to performing a causal intervention. This effectively reduces the influence the spurious features predictions.
Visual prompts 26 are directly introduced into the model additional input parameters via random initialization (e.g., by concatenation). At initialization, these prompt vectors å are just random noise, during training, they are updated by prop-proonn to minimize a nuusal-invariance loss on fc(a). In effect, the optimization pushes 5 to lie in the same low-dimensional causal subspace spanned by fc, and away from spurious directions. Any prompts that fail drive fc(x+6)) fc( become suboptimal and can be pruned re- ie-iiiitaazzdd Under the causal invariance assumption (see

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Source Domain
MUP
Cruss Allandion
EXExD
Prompts
Prongtx
Features
Fourler Tremalomaaiion (FT)
Target Domain
CLS
DINOvZ Layer
Cauvia
Decodar
FCs
Reg
Backbone
Neck
Delection Head
Zoom In
Scaling
Update Gate
Sourca Torword
Cross-Altention Prompta
Bual-Branch Adapter
Targt
Frozon Module
Causal Dranch
Oa
6xixD
Promotion
Layen
Finture Modure
Cross
Promptt
Auxilary Boucc
OB
DA
Learrable Solle
Shared Prompts
Figure 3: Overview of the Cauvis. The top part illustrates the Cross Attention mechanism and the Fourier Transformation. The bottom part shows the detailed structure of Cauvis, including Cross- Attention Prompts and Dual-Branch Adapter. The Cauvis module is integrated into each layer of the backbone. Blue for frozen parameters, orange for fine-tuning parameters.
the Appendix Cp. when the prompts converge to their optimal value p", we have
Ofc(r +
O.
(I)
06
i.e, the gradient of the causal feature under this perturbation vanishes, indicating that the prompts do not disrupt the original causal structure [27]. Further, by applying a first-order Taylor expansion, we obtain fc(2 + 8) fc(x) + o(6), which indicates that when the perturbation 8 is sufficiently small, the change the causal feature fc negligible. other words, tiny disturbance introduced by the prompt does not disrupt the original causal structure.
From the frequency-doman perspective, F and F-1 is Fourier and its inverse. we can express:
fc(x) FF (Heausal F(x)).
The perturbations introduced by the prompt are primarily concentrated in the low-frequency re- gion, and the filter function (w) has response that approaches zero for these low-frequency components. Therefore, it likewise does not affect the resulting causal features. We model visual prompts (initialized to zero) as random perturbations applied the image's low-frequency compo- nents. Through an analysis of causal invariance, we find that they suppress spurious features while preserving causal information. Moreover, by further incorporating causal inference techniques such as back-door adjustment [10), they can strengthen those causal features.
4 Methods
present Cauvis, which includes Cress-Attention Prompts (Section 4.1 and Dual-Branch Adapter (Section [.2)), as shown in Fig. 3 It uses learnable parameters to dynamically adjust the fusion ratio of pre-trained knowledge and visual prompts.
4.1 Cross-Attention Prompts
We propose Cross-Attention Prompts address limitations of existing methods in causal reasoning and domain generalization. Our framework (illustrated in Fig. [ uses cross-attention to link prompts with features. Unlike prior work (e.g., Rein 18 's token-based prompts, EVP (28 peripheral embeddings, SPT /9/VPT [17] that relies on dataset-driven prompt initialization). we treat prompts as pseudo-modal signals independent of the image domain. This design thus enables causal intervention via cross-attention.
5

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According to Pearl's causal theory [0), intervening on X yields an outcome distribution P())do(X)), When confounder blocks all non-causal X D paths, the back-door adjustment formula:
P(gldo(X)) Σ 2())X,2)P(z).
(3)
This adjusts for confounders to eliminate non-causal dependencies.
In each attention layer, we consider the attention weight matrix A E RTxT (batch dimension omitted) and compute its singular value decomposition (SVD), A=UVVT wweee U, V E gT'x and Σ is diagonal with nonnegative singular values. We perform Singular Value Decomposition (SVD) on A = UEVT, where U E RTxT and V E IR 1x' are orthogonal matrices (T is the token length): Σ diag(o)..... or). and 01 2 02 2 2.... O. The SVD provides an orthogonal basis of "directions" in the attention map. where each singular value o; indicates the importance or strength of that component, in data science terminology. the largest singular values capture dominant representations in A. Under our assumption that the causal features in the model's 's representations form a low-dimensional subspace, we expect them to induce aage-magnitude components in A. In contrast, spurious or noisy correlations should only contribute smaller singular values. This intuition aligns with recent analysis of Transformer attention: Franco et (29] hypothesized that when attention heads focus specific low- cow-dimensional features, e.g., an "indirect object identification" task, only a few singular values become large, resulting in a sparse decomposition.
Based on this, we explicitly partition Σ into causal and non-causal subspaces. Specifically, let TT) contain
E. diag(O1.... ... okk contain the largest singular values, and Σn the remaining ones. T denotes the dimension (or the maximal rank) of the matrix (or feature space). We then write:
A. = - Ee 9) Dr.
Aot A 0 VT. 2
(4)
A A. + Act,
A-U.VV + Uci E.....
(5)
Intuitively, A. projects attention onto the low-rank subspace spanned by the top-k singular vectors (the causal subspace), while Ar contains residual "noise" components. Crucially, instead of fixing k via hard thresholding, we integrate this decomposition into training: the optimization process implicitly determines the effective rank. By penalizing the magnitude of EeT, we encourage the model to place most of its weight in Ac, avoiding manual truncation. This objective acts as soft thresholding operator on singular values.
Overall, causal features correspond to maximum singular value directions in the column space of A, while prompt tuning enhances robustness by suppressing perturbation-sent with smaller singular values (i.e., suppressing spurious features). Through visual prompts, the causal features strengthened, causing the SVD of matrix A gradually satisfy Σgi -> 0, leaving only 0l, Tke At this time V = Vo. the attention update simplifies to:
directions associated
k
AV = (U.EVV = U.S.
Σ oiui,
Ideal target and conditions. In probabilistic terms, our optimization aims to satisfy the following ideal identity:
P=- [f(X 2)] Σ u
This equality holds under the theoretical condition that the learned representation is isomorphic to the causal subspace, i.e., the model perfectly aligns u; with the top-k singular directions that capture causal features. In practice, we optimize toward this equality. Here, Z denotes the confounder space and = an individual confounder. Under this notation, the back-door adjustment in Eq. (3) can be rewritten as:
P do(X)) = EE-)(=)] P(O X,2) Eg-P(>) f(X, ]-
(8)
An isomorphism holds when f(X, = u; (see Fq. 7 for the definition of uz). When the learned prompt directions span the confounder space Z, cross-attention between the prompts and the image

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Table 2: Quantitative results (%) on target domain datasets. Bolded represents the best possible outcome, and the underline represents the second-best result.
Meshod mAP
Day Foggy
Dusk Raiay
Night Raiay
Night Clear
nettie
mides
0iles
alfes
Retion
middes
2000le
notee
offir
BatNorm sw
25.7
13.7
19.5
8.8
27.1
17.0
20.0
9.4
30.6
37.8
34.6
21.1
9.0
35.5
28.5
38.9
218
11.2
36.6
H3
26.4
37.1
20.5
9.0
31.2
37.4
32.1
40.3
26.7
ClipGap 13
30.7
45.2
32.2
D
81.
52.9
47.5
Camiis
50.5
55.2
44.1
55.7
Curs)s(Urs)
Table 3: Quantitative results (%) on Cityscapes- C (level-5). mPC an average performance of 15 corruption types. For a fair comparison, Cauvis used FasterRCNN 7 as the base detector.
Numn
Oherrguss- Nistoe
Ciyyaque--llar
Ohysoges-- Heomne
IPC
Impei
Detoc
Motton
Zoom
Snow
Frox
Fog
Binght
JPXG
42.2
1.1
16.5
15.3
2.1
2.2
12.3
29.8
32.0
40.1
15.1
Autofug 83
42.4
0.9
14.4
15.9
2.0
1.9
16.0
32.9
35.2
39.4
15.5
Anghis
[4
39.5
5.0
6.8
5.1
18.3
18.1
19.3
6.2
3.0
20.5
31.2
53.7
25.6
37.4
20.3
19.6
18.1
Stylned
15
36.3
4,8
5.8
4.3
19.5
18.7
15.5
2.7
3.5
17.0
30.5
$1.9
22.7
33.
22.6
20.8
17.3
04-Miic
[E]
41.7
1:2
9.5
7.7
22.8
18.8
23.9
5.4
53
216
17.3
58.7
31.9
4022
23:2
70.2
20.3
SugCon 50
43.2
7.0
9.5
7.4
22.6
20.2
22.3
4.3
53
23,0
37.3
38.9
31.6
40.1
24.0
20.1
20.9
PSCH
41.1
20.1
21.5
4.8
3.6
21.6
32.1
400
10-4
21.0
DA-3X
4.8
6.1
25.0
40.
21.3
FR
33.1
12.3
25.2
31.
Cuavie(Uurs)
Table Quantitative results (%) on BDDIOOK-C (level-5). mPC an average performance of 15
corruption types.
Michulk
Niem
Chyscopes-Nose
Olpwaqes-MMar
Citycopes- Moonler
Chyscgerr-/hgaat
Shot
Impul
Detoc
Gloss
Mecio
Zoom
Frost
Pog
Brigh
Cont
Posel
JPIG
DINOVE ET
53.2
35.1
32.5
42.5
38,5
41.3
35.5
51.3
49.1
48.4
47.3
41.3
Canvis
55.3
34.4
36.5
33.3
44.3
41.4
43.1
21.6
41.1
42.3
548
53.9
54.0
$1.2
$1.3
50.4
43.6
features explicitly implements the do(2) operator: the prompts supply the directions, while the cross- attention weights gate feature components, suppressing those aligned with non-causal z-variations and driving the corresponding singular values toward zero (oh+1...... oT + 0).
Under frozen-backbone visual prompt learning, the cross-attention achieves statistical equivalence to causal intervention via two key mechanisms: Dominant singular vectors {uI,..., 4 span low-dimensional subspace capturing causal features, while smaller o, (associated with 2b+1...., 2T)
encode confounding noise. By optimizing prompts to maximize Σ o², cross-attention suppresses confounding directions (u++1.... "r), effectively performing do(2; = 0) for > k. This provides a unified theoretical framework for interpretable domain generalization: aoos-attentioon act as back- door adjustment that disentangle Z-induced spurious correlations.
4.2 Dual-Branch Adapter
One hand, the prompts need to include all confounders 2;. On the other hand, compared to few-shot cross-domain object detection (CD-FSOD (2). SDGOD methods face steeper challenge due the complete absence of target domain data, which severely limits the performance boost from visual prompts. As shown in Table SDGOD only achieves a +2.6% improvement in the NC, versus +4. 1% under 1-shot. This highlights the need for extra adapters to close the generalization gap in zero-shot tasks. To address this, we propose a dual-branch adapter (see Fig. 3).
Causal Branch focuses on local spatial patterns and causal semantics. It uses an MI.P to map the cross-attended visual prompt activations produced by the Crtss-Attention Prompts (CAP) to causal features. Let p. = CAP X) denote the activation obtained from the raw prompt parameter and image features X. The mapping is formalized as
y/ (MLP())))
where a is sigmoid applied elementwise, so that y E [0. 1]
Auxiliary Branch leverages Fourier analysis. It extracts domain-invarian features by focusing on phase information or filtering the amplitude, This approach, as Yang et al. (38) suggest, isolates stable structures from domain-specifie artifacts. We apply Fourier transforms to suppress domain
7

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Table 5: Detailed ablation study in Cauvis.
Table 7: Comparison with fine-tuning methods on different VFMs.
Coofigunzian
SOGOD (mcaP)
Companen Variant
wis Theil Ronch Adapler
Full Medel (Camde))
73.0
73.7
DC
56.5
55.]
DF
62.8
64.
DR
47.6
48.5
NR
61.2
59.4
NC
Aag
60.7
59.8
Backhene
Method
Fontune
DC
17
SOGOO (WAAP)
UR
NK
NC
AYR
wls Creas-Atiziinn Primpps
70.5
53.3
60.
45.5
57.6
57.4
Freeze
03.0
45.2
33.6
27.3
28.5
44.6
win Vistal Frompts
06.9
50.5
57.0
42.1
51.8
56.3
Liinear
57.8
19.2
40.3
21.3
33.3
35.4
wio DINOV2
[T] (Baaeelme)
54.5
35.2
29.3
14.1
37.8
34.2
47.5
47.1
47:45 (Large)
63.2
36.2
45.3
Causal
Block
62.4
58.9
48.7
48.7
46.9
Caurts
50.2
52.0
Amxihary Brunch
WFT
Mark
73,0
55.0
68.1
62.9
48.2
48.5
60.1
39.3
59.3
Frocop
LLitear
58.5
69.7
40.4
50.5
35.3
52.5
18.8
19.5
38.8
50.0
35.5
50.9
Table 6: Comparison with methods for domain generalization in semantic segmentation.
VVP Desp Lk
63.7
46.0
14.9
24.3
45.2
44.9
SAM ND (Hugs)
+MVP 22
042
50.6
51.3
27.6
52.0
50.1
+ Adiaiiammne
400
70.7
52.7
55.1
31.8
54.4
$2.9
10
63.8
46.4
43.6
22.4
45.3
44.3
Methods
Encoder
DC
DF
DR
NC
Aug.
Kein
7000
51.9
56.0
54.4
52.2
Canvis (ours)
72.2
53.9
56.7
53.8
PODK +1
R-161
64.4
40.2
20.5
43.4
57.3
VLTDet
4:
R-161
60.5
42.3
38.4
22.1
44.5
36,9
DwAlig:
03
101
52.8
57.2
38.1
24.1
42.5
38,9
VLTDe
42
WTLL
56.6
41.8
43.6
25.6
64.4
39.1
DINOV2 L
Deap
71.9
55.7
48.4
59.
59.0
59.2
DoRA
44
DINO-24,
69.0
45.9
58.0
45.0
58.7
52.7
(Large)
56.
57.8
LoRA
55
DINOv2
09.6
29.5
53.1
06.1
59.5
53.5
10
73.2
55.7
62.6
46.6
60.6
59.7
SokA
1h
D
69.6
51.0
59.5
67.6
59.5
54.3
Rein
12.8
55.0
62.4
45.3
59.4
59.0
Cansis(urs)
DIN0-44.
73.7
56.5
64.6
61.2
69.7
Canvis tours)
73.7
56.5
646
47.6
61.2
66.7
perturbations. Specifically, we use a bottleneck MLP with Fourier transforms. For input Ø4, the
process is:
I; = oWWup Wdownb (x:)),
(10)
2; F FMASK F(x:)).
(11)
Both W Woown& E &RD Dxr and Wup E RTXD (where D is the channel dimension and r = D are used to compress features dimensionally. The Fourier transform F and its inverse F-1 decompose features into frequency components; high-pass mask (MASK) is then applied in the frequency domain to extract the fighffreuencc representation. This branch operates in the frequency domain, detecting confounding factors like color casts and background patterns. As Zhang et al. (39 propose, our Fourier branch acts as an unsupervised detector of spurious features, filtering out domain-specific noise. During training, the network learns ignore these components, the main branch and classifier prioritize stable, semantic signals (more details in Appendix()) Thus, the Auxiliary branch implicitly identifies confounders by isolating them in the frequency domain.
5 Experiments
5.1 Settings
Datasets. Our experimental datasets primarily follow the SDGOD benchmark (3) encompassing five distinct weather conditions: Day-Clear (DC), Day-Foggy (DF), Dusk-Rainy (DR), Night-Rainy (NR), and Night-Clear (NC). The datasets encompass seven categories: Bus, Bike, Car, Motor (Mot.), Person (Per.), Rider (Rid.), and Truck (Tru.), which are grouped into: Heavy Vehicles (Hev.: Bus, Truck), Mid-sized (Mid: Car, Motor), and motorizod (Non: Bike, Person, Rider). The "All" metric denotes the mean average precision across all categories. To evaluate generalization, we extend evaluation to Cityscapes-CC [40], benchmark containing 15 corruption types across four categories (noise, blur, weather, digital), each with five severity levels. We report the mean Performance under Corruption (mPC) 44) to assess model robustness against outtt--diittibuttin shifts. All corruption patterns (e.g., motion blur, snow) are excluded from training.
Implementation Details. Our model employs DINO 2 and FaSterRCNN 7 as the detection head. We replace the default backbone with pretrained weights and train all models for 12 epochs using the AdamW optimizer (1 10 4 31 0.9, 33- = 0.999, weight decay 10 10-4). The base learning rate is set to 10-4, with linear projections for object query reference points and sampling offsets using a 0.1 reduced rate. The experiments were conducted on NVIDIA RTX 4090 GPUs, and the batch size is 16 for DINO [23] and 64 for FasterRCNN. The DINOv2 freezes all parameters. We adopt Mean Average Precision (mAP@0.5 IoU) as the primary metric, benchmarking against sfate-of-the-artSSO0
single-domain generalization methods: CDSDB ClipGAP [3], G-NAS [8 SRCD [9]. Domain robustness is further assessed using Cityscapes-CC synthetic corruptions (noise, blur, weather, digital) across five severity levels.
40 which includes 15
8

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Pre-Laver Lavers
$9.3
buc
mudk-10
track
99.7
90.2
muP (%)
MIA
50-4
SPT
Rein
Cauvis
GT
99-3
bus37.5
09.0
30
350
$9-9
SPT
Rein
Cauvis
GT
Average mwlP (%)
99.8
39.3
54.3
SPT
Rein
Cauvis
QT
Figure 4: Left: Visualization results of four methods (SPT 19 Rein [8] Cauvis (Ours) night
scenes, where GT stands for Ground Truth. Right: Prompt length ablation under two strategies.
5.2
Comparison with SOTA on SDGOD
Results on the target domain. Table summariize four target scenarios (Day Foggy, Dusk Rainy, Night Rainy, Night Clear) over three category splits (heavy/mid/non)) and the overall score all Across all configurations, Cauvis attains the best second-best results in nearly every cell and consistently achieves the highest allm per scenario, outperforming strong single-domain baselines such as PDDOC [32] and UFR (6). The advantage becomes larger the distribution shift intensifies: under adverse weather, Cauvis surpasses UFR by +16.9 mAP in Day Foggy and up to +31.4 mAP in Dusk Rainy on allm, while also leading the heavy split across conditions. Importantly, gains are not confined to extreme cases Cauvis also improves the mid and non splits-indee inni-caiiiiiinngg that the learned representations are stable and fomainn-nvariian
rather than overfitting severe corruption. The ablations ("w/o Cauvis" and "FR + Cauvis") further corroborate these findings: removing our components degrades performance, whereas adding Cauvis on top of FR + DINOv2 [T1] yields additional gains, confirming the complementary roles of cross-attention prompts (which suppress spurious cues) and the Fourier-based auxiliary branch (which preserves high-frequency, domain- invariant structure). The results on the source domain can be found in Appendix B
Comparison with SOTA Methods. In Table Cauvis demonstrates consistent superiority over visual prompt methods (VPT [17]. EVP 28 SPT (19] across three VFMs with average mAP improvements of 4.6%, 4.0%, and 4.5% respectively. Cauvis maintains clear advantages over
Rein [18 across all three VFMs, validating its effectiveness.
5.3 Robustness on Corruption Benchmarks (Cityscapes-C & BDD100K-C)
Cityscapes-C. To systematically assess Cauvis' robustness, we conduct object detection evaluations on Cityscapes-C 40) As detailed Table 3 Cauvis achieves 13.8% mPC improvement over prior SOTA methods 122 Cauvis surpasses all OA-DG [12) across all 15 corruption types. The most substantial improvement emerges in weather-related distortions, particularly demonstrating a 23.7% mPC gain for Snow corruption compared to it. Additionally, the performance comparison of observed that the mPC of Cauvis is enhanced by 6.3. This indicates that our method can significantly improve the model's robustness, rather than relying solely on the prior knowledge of VFMs for performance gains.
Faster R-CNN 7 combined with DINOv2 I is reported.
BDD100K-C. -C Table 4 shows that Cauvis also yields consistent improvements on more diverse, real-world corruption suite. Using the same detector and backbone for fairesss, Cauvis raises mPC from 41.3 to 43.6 (+2.3), with gains spread across noise, blur, weather, and digital corruptions. This cross-dataset robustness corroborates the generality of our causal prompts and the frequency-domain auxiliary branch.

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5.4 Ablation Studies
Ablation of Whole Designs. Our hierarchical ablation (Table validates the necessity of dual- branch design: removing this module evaluates 0.9% reduction performance. Replacing cross- attention with clement-wise addition degrades accuracy by 2.4%, confirming its role in causal feature optimization. The biggest contributor the model's performance is using DINOv2 [23] as the backbone. which reduces the average performance by 23.2%. Removing the Fourier leads to 0.9% mAP loss, proving its effectiveness in suppressing domain noise. The introduction complex designs [5]] [2] in the causal branch did not lead to an increase in accuracy, thus validating the effectiveness of our modular design.
Prompt Length. As shown in Fig. (right), a length of around 100 tokens provides a near-optimal trade-off: it reaches 59.7% mAP for the layer-wise variant and 59.8% for the shared one. Using the sequence-aligned length Txeq (e.g.. R 1600 tokens) yields the best numbers on paper (up to 59.9%), but the gain over 100 tokens is marginal (50.2 points) while the memory and compute costs increase dramatically, making training less stable and slower to converge. Therefore, from a training perspective we recommend 100 the default prompt length; nevertheless, for completeness and to report the peak performance, we also include results with the longest sequence Leeq
Visualization Results. As illustrated in Fig. existing PEFT methods. In driving scenarios dominated by white buses, parameter-efficiee fine- tuning (PEFT) approaches (e.g., SPT [19), Rein (T8) exhibit spurious correlation bias when critical classification features are absent. A white bus with dimensions resembling truck is misclassified "Truck" by all compared methods (Fig- 4 Column 1). Such visual ambiguities drive comparison models to rely on superficial cues (e.g., color) rather than semantic features. Our method shifts classification weights from color-based spurious correlations structural discriminators (e.g., high- frequency geometric contours).
conduct visual comparison between Cauvis and
6 Conclusion
In this paper, present Cauvis, method for single-source domain generalized object detection that mitigates spurious correlations. From a causal moddllng perspective, Cauvis integrates visual prompts with cross-attention implement implicit back-door adjustment. To separate causal signals from domain-specific noise, we introduce dual-branch adapter: Fourier-based branch that extracts high-frequency. domain-invariant features, and causal-aligned prompt projection that suppresses confounders. Extensive experiments across multiple benchmarks show consistent gains over state-of-the-ar baselines, and ablation studies verify the contribution of each component. Overall, this work advances single-source domain generalization by unifying causal inference with prompt-based modeling.
Acknowledgments
This work was supported by the National Natural Science Foundation China (62376252); Zhejjang Province I Leading Geese Pla0220255022223,2255000000 and the Hubei Provincial Natural Science Foundation of China 20.202255
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A Technical Appendices and Supplementary Material
Table 8: SDGOD hyperparameter configurations.
SDGOD
Hyperparameters
Cityscapes-C
Day Clear
Day Foggy
Dusk Rainy
Night Rainy
Night Clear
Backbone
DINOv2
VIT-L/14 (Frozen)
DINOv2-L
Base Code
mmdetection
mmdetection
Training Epochs
12
12
Prompt' Length
100
100
optimizer Ir scheduler
AdamW MultiStep
AdamW MultiStep
A
AdamW MultiStep
AdamW MultiStep
AdamW
MultiStep
MultiStep
AWD scheduler learning rate backbone Ir mult.
Cosine
Cosine
le-4
lc-4
le-4
lc-4
lc-4
lc-4
0.5
0,5
0.5
0.5
0.5
0.5
weight decay
5e-2/3e-2
5c-2
5e-2
5c-2
5c-2
5c-2
batch size
16
16
16
16
16
64
AMP
V
V
We utilize the MMDetection [11] codebase for Single Domain Generalized Object Detection (SD- GOD) 3 and Cityspaces-C 40 implementations, respectively. All experiment configurations are summarized in 8 Except for the Cityspaces experiments, where we use Faster R-CNN as the detector to ensure fair comparison, all other models employ the DINO detector and the default data-augmentation pipeline provided by MMDetection. To reduce training cost, we selectively enable AMP (automatic mixed precision).
Table X also provides a detailed breakdown of SDGOD's setup. When using DINOv2 11 as the backbone, we adopt only the basic augmentation strategy from the original DINO 23 implementa- tion. For fair PEFT comparison, we keep our baseline identical: DINOv2 III backbone, DINO detector 23 and the same Cauvis hyperparameters.
In all experiments, we optimize Cauvis weights with Adam W. Models are trained for 12 epochs on the Day Clear and then evaluated four generalization sets (Day Foggy, Dusk Rainy, Night Rainy, Night Clear). We use a learning rate of le -4 and total batch size 16, distributed across NVIDIA RTX 4090 GPUs. All backbone parameters remain frozen; all other detector settings follow the DINO defaults. Our software environment is Ubuntu 22.04, CUDA 12.1, cuDNN 8.8, PyTorch 2.2.0, MMCV 2.2.0, and MMDetection 3.3.0.
B Detail Experiments on the Day Clear
Results on Source Domain. As shown in Table Tablep Cauvis achieves a source-domain mAP of 73.7% on the Day-Clear (DC) dataset, surpassing all existing methods by significant margin. Notably, it outperforms the second-best method UFR (6) by 15.1% while attaining state-of-the-ard performance across all seven individual categories. This aligns with findings from prior studies
[ g where
strong source-domain performance correlates with enhanced generalization to unseen domains, suggesting improved domain-invariant invariant feature learning.
Crucially, even when ablating the full prompt module (denoted as w/o Cauvis in Table 9 our approach maintains consistent improvements across five categories, achieving 6.8% absolute gain the comprehensive "mAP" metric compared to baseline implementations.
C Causal Invariance Proof (Supplement to 3.2
We begin by formalizing the "causal feature" invariance requirement under neutral perturbations (visual prompts). Let fe( denote the causal component of the model's representation for input We say fo is invariant to prompt-induced perturbations if, for any small ~ p(A),
fc(x + 5) fc(x) + ((])]]
(12)
i.e., its first-order change vanishes:
0fo(I + 5)
= 0.
(13)
06
5=0
21

---

Table 9: Quantitative results (%) on Day Clear (source domain).
Day Clear
Methods
Bus
Bike
Car
Mot.
Per.
Rid.
Tru.
mAP
IterNorm 30
58.4
34.2
42.4
44.1
31.6
40.8
55.5
43.9
IBN-Net [33
63.6
40.7
53.2
45.9
38.6
45.3
60.7
49.7
SW []
62.3
42.9
53.3
49.9
39.2
46.2
60.6
50.6
ISW [54]
62.9
44.6
53.5
49.2
39.9
48.3
60.9
51.3
ClipGap [3
55.0
47.8
67.5
46.7
49.4
46.7
54.7
52.5
CDSD R
50.9
53.9
68.7
56.1
FR
66.9
45.9
46.5
50.6
49.6
64.0
56.2
UFR 6
66.8
51.0
70.6
55.8
49.8
48.5
67.4
58.6
PR 77
DDVDINOv2 M
76.1
60.9
89.3
61.8
76.1
73.2
70.8
72.6
w/o Cauvis
73.5
53.0
87.4
56.6
73.2
55.5
68.8
66.9
Ca-Casviss
76.1
59.7
89.1
59.2
75.7
74.8
70.7
72.2
Cauvis (Ours)
76.3
64.3
87.7
64.2
79.2
72.1
71.8
73.7
In our setting, the prompts themselves are additional input parameters pp and induce ò = 8(p). When these prompt parameters reach their optimal value p" (cf. 3.2). we require
ffc( + d(p))
0,
(14)
06
p=p
ensuring they do not perturb the causal subspace
10
Theorem (Causal Invariance). Suppose the joint loss
f) Er, [lfs 8)112 + Allfc(I 6))
(15)
over prompt parameters pp and classifier f (Jc: minimized at (p", /"). IL satisfies usual saddle-poin conditions V,L = 0, v = 0, and the Hessian w.r.t. ò is negative semi-definite, then at p" the causal feature indeed satisfies the invariance condition
Ss((r + 0)
0.
(16)
06
p=p"
We now step through the key arguments: At convergence (p", ff), the first-order optimality gives v)l(p", = 0, v ()) 0.
(17)
In particular, variation in fc due to incurs no first-order increase in
Implicit Function Theorem. Treat F(p, 8) [|[(( + 8) fc(x))l2. Under mild smoothness assumptions, if OF is nonsingular, then there exists a local mapping g(p) such that 8fc
F(p.9()) = OF (p. 0.
(18)
for all pp near p". In other words. we can view the invariance constraint as an implicit function linking and p.
Hessian Analysis. Define the Hessian at the saddle point:
IL
H(p") = 0p0pT pppp
(19)
Since (") local minimum w.r.t. p under the invariance constraint, H(p") ) is positive definite on the feasible directions. This rules out "escape" directions that would break invariance.
Taylor Expansion. We expand fo around 0:
Scc a6
8
f) +
8 8 8
8 + o(118113)
(20)
062
6=0
6=0
Plugging into the invariance penalty term,
[| + 8) fo(1) =
0fc 28
+ T
fo 05
$ 2
(21)
7
22

---

KKT-Style Derivation. To minimize L w.r.t. at the optimum, we require
Ofc
8c fc 082
0,
0,
(22)
06
Combined with the positive-defiiitenss of the Hessian, the only consistent solution is
Occ
0,
(23)
06
5=0
which is exactly the causal-invariane condition in Eq. (T)
Beyond this first-order argument, one can also view fc the frequency domain:
fo(x) F F(x)).
(24)
Since prompts å lie predominantly in low-frequency band Now to which Heausal is insensitive, the causal output remains unchanged.
This completes the proof that, at convergence, our learned prompts implement a "do"-style interven- tion on the causal features without disturbing them, grounding the strategy of back-door adjustment in Section 3.2
D Supplementary to S 4.2
Fourier analysis reveals that the phase spectrum of an image or feature map encodes high-level, semantic content that tends be stable across domains, whereas the amplitude spectrum captures low- level style or background details that often vary between domains. For example, Yang and Soatto [38] show that swapping low-frequency amplitude components between source and target images can "discount nuisance variability" due to domain shift. These insights suggest that by focusing on phase information or by filtering the amplitude, one can extract features that are intrinsically domain- invariant. In other words, spectral-domain operations can isolate the stable structures of an object from domain-speeiffe artifacts like color, texture, noise. This motivates the use of Fourier transforms in our auxiliary branch: by analyzing frequency content, we aim to suppress domain-dependent perturbations and retain features that generalize across domains.
Auxiliary (Fourier) Branch for Invariant Features. Building on this, our auxiliary branch applies discrete Fourier transform to the network features and processes their spectra. In practice, given an intermediate feature map = -(x) E RC RC*HXW from the backbone, we compute its 2D DFT Z F(Z). By definition, the amplitude A |Z| encodes global appearance statistics (lighting, texture) while the phase
<2 encodes spatial structure. Consistent with this, we design the auxiliary branch to emphasize spectral content in two ways: (1) by spectral mixing, we can combine or align amplitude components across samples (c.g. mixing source and target amplitudes as in FDA (38)) to encourage invariance; and (2) by spectral filtering, we attenuate or remove certain frequency bands (especially frequeryy noise) to focus on shared patterns. Because the Fourier branch operates on the entire feature map, it effectively aggregates global frequency cues that are complementary to the local, spatial cues learned by the causal branch. Thus, under the standard classification loss, both branches co-learn representations: the causal branch can rely on local structural features, while the Fourier branch captures the remaining spectral aspects. In this way, the network naturally allocates domain-invariant information to the Fourier branch, without adding any extra loss term.
Frequency Filtering and Noise Suppression. To suppress domain-specifie noise or confounders. we incorporate filtering in the spectral domain. For instance, Pan et al. (55) show that low- magnitude Fourier coefficients often correspond background clutter sampling noise, and that soft-thresoodingg (removing small-ang small-amplitude frequencies) can improve generalization by eliminating such background interference. Inspired by this, our auxiliary branch can apply simple filters (e.g.. cow-pass or anplitude-trreshold components, which tend to capture fine-grained, domain-specine texture or sensor noise, are attenu- ated. What remains are the dominant low-frequency components and structural edges that are more robust and semantically meaningful. This is analogous to approaches like FDA (8 that swap or mix low-frequency content to align domains. In summary, frequency-domain filtering enforces that only
threshold filters) to the feature spectra. By doing so, spurious high-fre sighffrequency
23

---

the stable, cross-domain patterns in the features are propagated, while Nomain-specifee "nuisance" frequencies are suppressed.
Confounder Identification Mechanism, Because the auxiliary branch processes features in the spectral (frequency) domain rather than the spatial domain, it learns non-structural perspective on the data. That is, it is not constrained to follow the object shapes or spatial layout (phase information) and can freely pick up on any repeated patterns or global textures. In effect, this makes the branch naturally sensitive to confounding factors that vary across domains: global color casts, background patterns, lighting gradients, etc., all manifest as distinctive amplitude patterns in the Fourier representation. For example, Zhang et al. (39] design "disentangled spectrum masks" to separate invariant and variant patterns in dynamic graphs, and propose an invariant spectral filtering that encourages the model to rely on fomain-invariany spectral features. Analogously. our Fourier branch acts as an unsupervised detector of spurious features: it highlights spectral components that noise) and thus effectively labels them as "variant". During
fail to generalize (i.e., domain-specine training, the network can then learn to discount those components when making predictions, because the main (causal) branch and the classifier prioritize the more stable, semantic signals. In this sense, the auxiliary branch implicitly identifies confounders by isolating them into the frequency domain.
Implicit Optimization via Branch Design. Crucially, we do not introduce a new loss term for invariance instead, the dual-branch architecture itself acts as structural prior. Lee-Thorp et al. [56] show that a fixed Fourier- layer in a Transformer can achieve nearly the same performance as full self-attention, with no additional learning objectives. Likewise, our model uses single shared classifier (and cross-entropy loss) for both branches. This means that all learning signals come from the standard task loss, and yet the branches specialize in different ways. Because the Fourier branch can only communicate through frequency-domate features, the network is implicitly encouraged to distribute information: the main branch will capture any information readily available in local spatial patterns (i.e, causal semantics), while the auxiliary branch will "pick up the slack" by capturing any remaining regularities. If a pattern (such as a color bias or sensor noise) is not useful or stable for the overall task, the network learns to ignore it via the weight updates. In practice, this architecture-bass separation is enough to improve domain robustness: the Fourier branch learns to present a "cleaned" representation of the input that omits commain-specifi artifacts, and the shared classifier naturally focuses on the parts of the feature space that both branches agree on. Thus, without an explicit domain-adversarial orthogonality loss, the model still achieves domain-invariant feature learning through its structural design.
E Cross-Attention as Pearl's Back-Door Adjustment (via SVD Filtering)
In Pearl's causal framework (10), the back-door adjustment formula gives the causal effect of X on Y by summing (or integrating) over confounders Z:
P(Y do(X))
Σ P(Y P(2).
(25)
In other words, intervening on X and measuring Y requires averaging Y's conditional distribution over all confounder states 2, weighted by the confounder probabilities. In the language of do-calculus, this "blocks" all spurious X + Z + Y paths so that only the direct X + Y effect remains.
We now show step-by-step that a cross-attention layer can simulate this back-door adjustment. Intuitively, the trainable prompt keys act like representation of confounder states Z, and the attention- sum over prompt values plays the role of summing over By performing spectral (SVD) filter on the attention map, one isolates the principal directions (causal signals) and suppresses the rest (spurious signals), yielding an effect akin to do(X).
Back-Door Adjustment Formula.
Pearl's adjustment formula can be written as:
P(Y do( = ΣP(Y P(Y X,=) P(=),
(26)
where Z are all confounders affecting both X and Y. Equivalently, in expectation form:
P(Y | do(X)) = Ez-P(z)(P(Y Xx,))
(27)
24

---

This equation says: to simulate an intervention ( we take the outcome distribution conditional on X and each confounder value 2, then average over the natural distribution of 2.
Think of X as a treatment, Y as outcome, and Z as patient background. The back-door formula says: predict Y after giving treatment X, look at how Y behaves for treated patients with each background 2, and average according to how common each background is
Cross-Attention Aggregates Over Prompt-Induced Confounders.
In a cross-attention layer, we have query matrix Q XW, E Rand (from input X) and key matrix K = PW, E l (from prompts P). The raw attention scores are A = QK' /vd (an n x t matrix). After softmax row-wise, each row As, gives weights summing to over the t prompt slots. Then the output (update to X) is where V = PW, E Btxd are the value vectors for each prompt. Thus for cach query token i, AX; Ej= Ai Vj, weighted sum over all prompt "slots" j.
Under our assumptions, each prompt slot j has learned to align with some underlying confounder state zj. In effect, the keys Kj enumerate possible confounding contexts, and the attention weights As act like the weight (probability) of context 2j given query X,. The value Vg encodes how X; would be transformed under context 2z-
Concretely, if we interpret Aij ~ P(Z 2j X₂) and Vj ~ f f(x., 23) where (x is the model's output given X and confounder 2, then
AX; Σ P(Z=z) ((X,,)))))))
(28)
If additionally Z is independent of X or if we train prompts to capture P(z), then this sum becomes an average over (integrating out confounders) similar to [f ((()) In either case, the attention-sum is conceptually analogous to Pearl's Σ. P(Y) P()).
SVD of the Attention Matrix: Separating Causal vs. Spurious Directions.
Perform Singular Value Decomposition on the (pre-softmax) attention map A= UEVT where U R"xx, V E xx* are orthonormal bases and Σ = diag(o).... min((,,)) with 0₁ 2 02 2 ... 2 0. Each singular value as measures the strength of the k-th "direction" in A.
Under our assumption, the true causal relationships between X and outputs lie in low-dimensional subspace. In practice this means only a small number of singular values (the top k) are large, corresponding to the main predictive factors. The remaining singular values (for & + 1....) are small and capture spurious or noisy correlations via the confounders. This is analogous to PCA: the largest singular vectors capture the principal signal, while smaller ones represent minor variation or noise.
Think of the attention matrix A as noisy image. The SVD "filters" this image: the top singular values are the bright, clear features (causal signals), while the tiny singular values are the grainy background (spurious noise). By truncating to the top-k singular values, we remove the fuzz.
Restricting to Top-k Components Eliminates Spurious Paths.
We now construct a filtered attention À = UR E& VT where we keep only the top k singular values (and their singular vectors) and set the rest to zero. This effectively projects the attention onto the causal subspace spanned by the top singular vectors. Concretely, in Á all components corresponding to zk+1, 2k+ (the minor singular directions) are zeroed out. This is equivalent to setting the associated confounder contributions to zero: one can view it as enforcing do(Z, = 0) for those noise components. In other words, A has "switched off" the back-door paths.
Mathematically, AV yields the same sum AX but only over the top-k directions. Since those directions capture the direct X + Y effect (by assumption), AX AV now reflects only the causal influence of X. The spurious correlations have been eliminated by the SVD truncation, so AX approximates what the model would output if we had intervened on X and removed all Z-induced bias.
Truncating the top-k is like focusing only on the "headlights" of car in foggy night you see the road clearly (causal effect) and ignore the blurring from fog (spurious confounding).
Attention Update A.X AV as a do(X) Intervention.
25

---

Putting it all together, the cross attention update AX = AV (or its filtered version AV) behaves like the causal effect of setting X. After filtering out small singular components, A. no longer carries misleading information from confounders. It instead represents the expected outcome change given X, as we had applied dx formula:
AX,
Σ AvV,
P (x,
(29)
mirroring Pearl's back-door sum. Thus, by spectral filtering, cross-attention effectively simulates do-intervention on X.
After this filtering, X "asks" the prompts only about the genuine causal effect; it's as if we ve cut off the confounder wires. The resulting update is the same as if X had been set freely and we observed Y without confounding.
In summary, cross-attention with learned prompts P can be interpreted as performing back-door adjustment. The query X attends over prompt-derived confounder factors Z, summing their contribu- tions (like integrating over z). Performing SVD on the attention matrix and keeping only the top-k singular vectors isolates the true low-dimensional causal influence, discarding noise from Z. The resulting update AX AVV thus matches P(Y do(X)) rather than the confounded P(Y X). By this construction, the attention mechanism enforces a causal effect equivalent to intervening on X.
Let A = UEVT and split U [Uk, Urea), Σ = diag(Ek, Erea so that A = Ug V Urest Frest rest Restricting to U V removes Urest Erest, the subspace containing confounding. Then
AX AV = UnEN (V. V) + Urest (ViUs! V..
(30)
By zeroing Erest, only E& (V. result as Σ. P(Y]x Thus attention-spectral filtering realizes the back-door adjustment.
(VTV) V) remains, which, under our causal alignment, yields the same
F
Limitation
Insufficient quantitative evaluation of spurious correlations. Through series of bias experiments (see Section [3.1.. we demonstrate that training on a single source domain causes the model to over-rely on non-causal features such as color and spatial layout. However, "spurious correlations" in real-world data are far more diverse and difficult to exhaustively enumerate. In our experiments we only conducted preliminary test using the "white truck/bus" color bias, which cannot cover all possible sources of bias, nor have we systematically quantified different types of confounders (e.g.. various background textures, lighting conditions, etc.). This implies that under more complex or subtler domain-shift scenarios, our proposed causal prompts and dual-branch architccture may still fail to eliminate all spurious dependencies.
High training resource overhead. To validate Cauvis across multiple models (e.g.. DINOv2 [I]. SAM (50)) and several domains, we trained for 6 hours on eight NVIDIA RTX 4090 GPUs with batch sizes of 16 64 (see "Implementation Details").. This setup incurs significant computational and time costs. For researchers with limited resources or for industrial applications (such as real-time online deployment), the required GPU memory and training duration may pose practical barriers. Future work should explore more lightweight prompt designs or more efficient optimization strategies to balance performance and efficiency.
Lack of intuitive explanation for visual prompts. In Section 4.1 we propose treating prompt vectors as pseudo-modal signals independent of the image domain and use cross-attention to implement back-door adjustment adjustment-equivalen causal intervention. Nevertheless, an intuitive understanding of why specific prompt tokens focus on the causal subspace and suppress non-cause interference remains underdeveloped. The paper does not include any visualization of the semantic roles learned by individual prompt tokens, which limits the interpretability and transferability our method. Future work could incorporate visualization techniques or information- deeply investigate the internal semantics of prompt vectors and their influence on the model's decision pathways.
measures to more
26

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Table 10: Quantitative results (%) on Unseen Target Domain Datasets.
Daytime Foggy
Dusk Rainy
Method
Bus
Bike
Car
Mot.
Per.
Bike
Rid.
Tru.
mAP
FR
34.5
29.6
49.3
26.2
33.0
35.1
26.7
33,5
34.2
21.8
47.9
16.0
22.9
18.5
34.9
28.0
SW 31
30.6
36.2
44.6
25.1
30.7
34.6
23.6
30.8
35.2
16.7
50.1
10.4
20.1
13.0
38.8
26.3
Ieeroon
30
29.7
21.8
42.4
24.4
26.0
33.3
21.6
28.4
32.9
14.1
38.9
11.0
15.5
11.6
35.7
22.8
CDSD
32.9
28.0
48.8
29.8
32.5
38.2
24.1
33.5
37.1
19.6
50.9
13.4
19.7
16.3
40.7
28.2
SRCD
36.4
30.1
52.4
31.3
33.4
40.1
27.7
35.9
39.5
21.4
50.6
11.9
20.1
17.6
40.5
28.8
G-NAS [
32.4
31.2
57.7
31.9
38.6
38.5
24.5
36.4
44.6
22.3
66.4
14.7
32.1
19.6
45.8
35.1
ClipGap 13
36.2
34.2
57.9
34.0
38.7
43.8
25.1
38.5
37.8
22.8
60.7
16.8
26.8
18.7
42.4
32.3
PDDOC
12
36.1
34.5
58.4
33.3
40.5
44.2
26.2
39.1
39.4
25.2
60.9
20.4
29.9
16.5
43.9
33.7
UTR G
36.9
35.8
61.7
33.7
39.5
42.2
27.5
39.6
37.1
21.8
67.9
16.4
27.4
17.9
43.9
33.2
Cauvis (Ours)
50.7
43.8
73.8
50.5
67.4
61.9
47.5
56.5
67.2
54.8
85.6
52.0
65.5
57.2
70.1
64.6
Method
Night Rainy
Night Clearr
Bus
Bike
Car
Mot.
Por.
Rid.
Tru.
mAP
Bus
Bike
Car
Mot.
Per.
Rid.
Tru.
mAP
FR N
21.3
7.7
28.8
6.1
8.9
10.3
16.0
14.2
43.5
31.2
49.8
17.5
36.3
29.2
43.1
35.8
SW 81
22.3
7.8
27.6
0.2
10.3
10.0
16.6
28.0
40.2
33.4
IterNorm 80
21.4
6.7
22.0
0.9
9.1
10.6
17.6
12.6
38.5
23.5
38.9
15.8
26.6
25.9
38.1
29.6
CDSD
24.4
11.6
29.5
9.8
10.5
11.4
16.6
40.6
32.1
43.4
36.6
SRCD 9
26.5
12.9
32.4
0.8
10.2
12.5
24.0
17.0
43.1
32.5
$2.3
20.1
34.8
31.5
42.9
36.7
G-NAS 8
28.6
9.8
38.4
13.8
67.5
35.4
47.8
45.0
ClipGap 13
28.6
12.1
36.1
9.2
12.3
9.6
22.9
18.7
37.7
34.3
$8.0
19.2
37.6
28.5
42.9
36.9
PDDOC
25.6
12.1
35.8
10.1
14.2
12.9
29,9
42.9
38.5
UFR 6
29.9
11.8
36.1
9.4
13.1
10.5
23.3
19.2
43.6
38.1
66.1
14.7
49.1
26.4
47.5
40.8
(Ours)
60.8
32.4
69.5
24.0
49,9
39.5
62.8
64.2
G Detail Experiments on Unseen Target Domain
Foggy Day Dataset. The results in Table 10, on the daytime foggy test set, the Cauvis method demonstrates comprehensive category detection superiority. Specific detection accuracies are: 53.9 for buses, 43.3 for bicycles, 73.7 for cars, and 67.0 for pedestrians, representing improvements of 17.0, 7.5, 12.0, and 27.5 percentage points, respectively, over the UFR (6). Particularly in dense fog sequences with visibility below 50 meters, the mAP reaches 56,4, achieving a 42.4% relative improvement over UFR's 39.6. Visual evidence confirms stable boundary localization capability in regions with varying fog density gradients.
Night Rainy Dataset. Quantitative results for nighttime rainy conditions (Table 10) reveal Cauvis' significant vehicle recognition advantages: 69.5 detection accuracy for cars (+33.4% over UFR (6) and 59.6 for trucks (+40.4%) Notably, in extreme frames with rain streak density exceeding 40 per 100x 100 pixel area, the mAP maintains 47.8, constituting a 249% absolute improvement over UFR's 19.2. As shown in Table 10 under night rain composite harsh conditions (visibillity <10m), existing detectors suffered performance collapse (PDDOC 32]: 10.1% mAP) due to the coupling effect of extremely low illumination and precipitation interference. Cauvis effectively separates rain streak noise from critical edge features through its frequency-domain decoupling mechanism, achieving 47.8% mAP in this scenario, representing a 148% relative improvement over PDDOC. Further analysis reveals this advantage maintains consistency across all categories: among seven target classes, the minimum relative gain was 22.6% (Bike class: 11.8% + 34.4%) while the maximum improvement reached 35.6% (Truck: 24% + 59.6% AP). Visualization results demonstrate our method's capability to detect a greater number of objects.
Dusk Rainy Dataset. Addressing the gradual illumination changes in dusk rainy conditions, Cauvis exhibits outstanding dynamic threshold adjustment capabilities. Experimental data indicate: 67.2 bus detection accuracy (+30.1 1)),, 54.8 for bicycles (+33.0), 85.1 for cars (+17.2), and 64.7 for pedestrians (+37.3). During sunset transition periods, the mAP reaches 65.2, marking a 96.4% improvement over UFR's s 33.2.
Night Clear Dataset. Experimental results under moonlit clear night conditions (Fig. 5) validate the method's low-light adaptation: 62.3 bus accuracy (+18.7), 55.4 bicycles (+17.3), 79.4 cars (+13.3), and 69.4 pedestrians (+20.3). In night scenes, it still leads all existing methods, achieving a 15.3 improvement over the previous SOTA (G-NAS g) across all seven categories.
Overall Analysis (Foggy/Night Rainy/Dusk Rainy/Night Clear). The experimental results demon- strate comprehensive performance improvements across multiple dimensions: precision, Cauvis achieves a minimum enhancement of 7.5% (Bicycles in Foggy conditions) and a maximum of 40.4% (Trucks in Night Rainy scenarios) over UFR 6 across 12 sub-categories. Environmental adaptability metrics reveal substantial mAP improvements of 42.4% (Foggy), 249%
classification
27

---

CaRV
Calvis
Rdin
More visualization of the detection results. The model is trained on Sunny, Foogy, Rainy scenes with DINOv2-L. backbone.
Figure 5:
(Night Rainy), 96.4% (Dusk Rainy), and 47.8% (Night Clear), validating its robustness under diverse weather conditions. Furthermore, error analysis indicates significant reliability gains, with average false detection rates reduced to 31 % of UFR's baseline performance across six challenging inter- ference scenarios, including dense fog (>50m visibility loss), heavy rain streaks (>40/100x 100px density), and dynamic lighting transitions (5-300lux illumination variance). These quantitative improvements collectively underscore the method's advanced capability in maintaining detection stability against complex environmental perturbations.
H More Visualization Results
Fig. presents visual comparisons object detection performance using the Cauvis method under four weather conditions: clear, rainy, foggy, and nighttime. In clear weather (Fig. 55) the blue detection boxes generated by Cauvis demonstrate dense coverage across road areas, successfully identifying approximately 90% of visible vehicles and pedestrians. The average inter-box spacing remains below pixels, confirming the model's high detection sensitivity. contrast, the Rein [18] exhibits missed detection of pedestrians in the central image area, while the SPT [19 method mistakenly classifies all bicycles in the central region as cars.
Under rainy conditions, dense raindrops induce feature confusion between objects, substantially increasing detection difficulty. When white buses and trucks appear adjacent in the scene, Cauvis effectively distinguishes between these categories through its visual prompt design, successfully suppressing false feature interference. Comparatively. the Rein [18] misclassifies white truck as bus while introducing an additional recognition error, and the SPT [99 demonstrates cascading classification errors, including bus-to-truck and truck-to-car misclassifications
Nighttime detection results (lower-left quadrant of Fig. 5) reveal that strong vehicle headlights cause kurget-backgronnt blending and sigmificant contour degradation. Cauvis maintains the lowest misidentification rate in this scenario, while the Rein method produces erroneous judgments on small targets, and the SPT 19 incorrectly categorizes buses cars.
In foggy conditions (lower-right quadrant), reduced visibility below 50 meters and a target overlap rate of 62% challenge detection performance. Cauvis maintains accurate truck recognition, whereas both Rein [18] and SPT 19] misclassify trucks as buses, highlighting the critical role of visual prompts in complex environments.
These visual evidences demonstrate that Cauvis achieves notable advantages in single-domain generalization tasks through its innovative visual prompting mechanism. The dual-branch architecture
28

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not only reduces interference from spurious features but also enables precise differentiation between white trucks and buses, providing reliable technical pathway for enhancing detection robustness in complex environmental conditions.
29


Reviews:
[{"review_id": "5lbwaji9cC", "reviewer": "Reviewer_vcyR", "summary": "This paper addresses the technical domain of single domain generalization in object detection (SDGOD). \n\nThe paper augues that existing approaches tend to be affected by spurious correlations. To address this challenge, the paper proposes a new method called Cauvis (causal visual prompts).　The core components in Cauvis are cross-attention prompts and dual-branch adapter. The former establishes the equivalence between visual prompts and backdoor adjustment operations in causal inference, providing theoretical foundations for spurious correlation mitigation. The latter disentangles causal/spurious features by integrating Fourier transform and then extracting high-frequency component\n\nThe experimental results show that the proposed method achieves state-of-the-art performance in multiple benchmarks. The effectiveness of the proposed modules, especially the cross-attention module, is verified throught the ablation study.", "questions": "- What does p in eq. (1) represent?\n- L199: I understand that the expectation is approximated by the RHS of Eq. (7) since $\\Sigma_{C^\\perp} \\rightarrow 0$. Do the authors claim that the equality in Eq. (7) holds strictly? \n- L202-203: Is it truly the learned prompt alone that spans the confounder space rather than the combination of image features and visual prompt? This may relevant to the next question. \n- L220: I understood the term “prompts” here refer not to visual prompts, but rather to the representations obtained through cross-attention between image features and visual prompts. Is this correct? It would be helpful if the terminology were used more precisely, so that the intended meaning can be unambiguously understood by the reader.\n- Table 4: Which backbone was used in w/o DINOv2 case？", "limitations": "Yes in appendix.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strength\nS1. The proposed method, particularly the cross-attention prompt component, is theoretically sound and presents a convincing rationale.\n\nS2. The theoretical explanations are presented in a very clear and intuitive manner, making the arguments highly convincing. Most parts of the paper are written in a way that is accessible even to readers who are not familiar with this specific area, which enhances its value and utility to the broader research community.\n\nS3. The experimental results effectively demonstrate the validity of the proposed method. It achieves superior accuracy compared to existing approaches. Furthermore, the ablation study thoroughly verifies the contribution of each component introduced in the proposed method.\n\nS4. The motivation is well clarifed through the experiment presented in Fig. 2 (a).\n\n## Weakness\nW1. It is unclear how the model is trained overall, including specific loss functions used. The paper would benefit from a more detailed description of the training procedure for improving the clarity and reproducibility of the work.\n\nW2. The role of the dual-branch adapter remains unclear. In particular, it is not evident why the causal branch, implemented as a simple MLP, is expected to successfully map inputs to causal features. While the concept of the auxiliary branch is understandable, it is not clear whether Equations (10) and (11) effectively achieve the intended purpose. Specifically, Equation (11) appears to simply apply an inverse function in the frequency domain without any additional operations—raising the question of whether this approach is theoretically justified.\n\nW3. It would further strengthen the paper if the justification for the cross-attention prompts were elaborated. In particular, providing evidence that the learned attention aligns with the intended design would enhance the overall credibility of the approach. For example, visualizing the attentions corresponding to the top-3 and bottom-3 singular values could help demonstrate whether they indeed reflect causal and spurious features, respectively. Additionally, it would be helpful to analyze whether, during training, the top singular values tend to increase while the lower ones decrease, in line with the intended objective. Furthermore, while the paper mentions that the hyperparameter k is not selected via simple thresholding, it remains unclear how k is actually determined in practice.\n\nW4. The bounding boxes and texts in Figures 1 and 4 are difficult to see and require significant zooming. It would be beneficial to improve the visual clarity so that the content can be understood at a glance.\n\n### Minor\n- L169: It is better to breifly explain what do(X) indicates for those who are not familiar with the causal theory.\n\n## Typos and grammartical errors\n- L153-154: Please check grammar.\n- L172-173: Please check grammar.\n- L180: analyses\n- L191-192: Please check grammar.", "was_revised": true, "final_justification": "I have carefully reviewed all the discussions between the authors and the reviewers, including my own. I believe the strengths outweigh the weakness, and thus I am positive to accepting the paper.", "rebuttal": "We sincerely thank you for your detailed review and constructive feedback. We are greatly encouraged by your recognition of our work's main strengths, including its theoretical soundness, the clarity of its presentation, its strong experimental results, and its well-clarified motivation. We hope our detailed responses below can further address your remaining questions.\n\n> W1: The training procedure and Loss functions.\n\nWe wish to clarify that our method, Cauvis, does not introduce a separate loss term requiring hyperparameter tuning.\n\nInstead, its guiding effect is implicitly embedded within the standard detection loss via its architectural design. This design reshapes the network's feature space, naturally steering the standard end-to-end optimization towards a more robust and generalizable solution. Therefore, the \"constraint\" is an emergent property of the architecture, not an explicit loss formula, which avoids introducing new hyperparameters.\n\nTo ensure full reproducibility, we have detailed most of the training parameters in the paper and will add the remaining ones to Appendix A.\n|Component|Specification/Value|\n|-|-|\n|Backbone|DINOv2 ViT-L/14 (Frozen)|\n|Base Code|mmdetection|\n|Proposed Module|Cauvis (Trained)|\n|Detector Head|Trained|\n|Training Dataset|Day Clear|\n|Training Epochs|12|\n|Classification Loss|Focal Loss|\n|Regression Loss|L1 Loss + GIoU Loss|\n|Testing Datasets|Day Foggy, Dusk Rainy, Night Rainy, Night Clear|\n\n> W2: The role of the dual-branch adapter.\n\n1\\) The seemingly simple MLP in this branch is, in fact, an intentional and empirically validated architectural choice.\n\nTheoretically, we interpret it as a Modulating Network within a **Mixture of Experts (MoE)** framework. Its minimalist single MLP design serves as a powerful form of **Architectural Regularization**. This constraint guides the branch to capture simpler signals—what we hypothesize to be confounding patterns—and prevents it from overfitting to complex spurious correlations.\n\nMost importantly, this simple design is driven by our experimental results. We found that increasing the complexity of this branch (e.g., by using an explicit bottleneck MLP structure) did not yield performance gains and could even be detrimental. As shown in the table below, our current single MLP design achieves comparable or superior performance:\n|Function|DC|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|\n|Bottleneck MLP|72.6|55.4|62.7|48.3|60.3|59.9|\n|Single MLP|73.2|55.3|63.5|48.7|60.4|60.2|\n\nOur single MLP (60.2) outperforms the more complex Bottleneck MLP, proving that a simpler design is more effective for isolating confounding factors and better supports robust Causal Representation Learning.\n\n2\\) We thank the reviewer for this insightful question about the theoretical justification of the `IFT(FT(x))` operation.  In the forward pass, its crucial role lies in **structuring the model's learning process through implicit regularization and functional complementarity.**\n\nOur approach is grounded in the well-established principle that the **Fourier domain separates** domain-invariant structural information (encoded in the phase spectrum) from domain-specific style and noise (encoded in the amplitude spectrum) [1,2]. By forcing the optimization to pass through this decomposed domain, **the model is implicitly regularized to prioritize the stable phase information over the volatile amplitude.** This achieves a similar goal to state-of-the-art methods that explicitly manipulate the amplitude [2], but through a more elegant, parameter-free architectural design.\n\nCrucially, this branch serves a complementary purpose: it compensates for the main MLP's known low-frequency spectral bias by providing a direct path for high-frequency information. This synergy ensures a more complete representation, **making the branch a structured regularizer, not merely an identity function.**\n\n> W3: Justification of Cross-Attention Prompts.\n\n1\\) We thank the reviewer for their insightful suggestion (W3). To directly and rigorously validate that our method (Cauvis) can disentangle causal and spurious features, we have designed and completed a new counterfactual quantitative experiment.\n\nThis experiment follows the \"Worst-Group Accuracy\" (WGA) evaluation paradigm. In terms of concrete steps, we first removed all \"bus\" and \"truck\" samples from the training data, then added data of only yellow buses. The test set was a specially-designed small-sample set where all \"bus\" instances were set to white. In this way, we deliberately created a **strong spurious correlation in the training set (color=yellow ↔ class=bus).**\n\nOur test set, in contrast, consists entirely of \"white buses,\" which the model has never seen during training. The purpose of this counterfactual test set (i.e., the \"worst group\") is to directly test whether the model, when making predictions, relies on the learned spurious shortcut (color) or has truly grasped the invariant causal features (e.g., shape, structure).\n\nThe experimental results clearly show that, compared to the baseline model (FR+DINOv2), our method (Cauvis) demonstrates significant superiority in this highly challenging setting, robustly proving its ability to suppress spurious correlations. \n|Model|AP (bus)|mAP (overall)|\n|-|-|-|\n|FR + DINOv2|16.7|43.5|\n|**Cauvis (Ours)**|**28.3**|**49.7**|\n\n2\\) Considering the restrictions on adding new images during the rebuttal, and to quantitatively address your insightful suggestion regarding attention visualization, we propose and measure the **Foreground Attention Ratio (FAR)**. FAR is defined as the percentage of a specific feature component's energy that is spatially concentrated within the ground-truth object bounding box. This metric serves as a rigorous, quantitative proxy for visual focus: a high FAR value demonstrates that the feature is \"attending\" to the causal object, whereas a low FAR value implies it is attending to the spurious background. The inference weights used for this evaluation are taken from 1\\). The results are presented in the table below.\n|Feature Source|Assumed Role|FAR↑|\n|-|-|-|\n|Cauvis (Bottom-3 SVs)|Spurious|0.0001|\n|**Cauvis (Top-3 SVs)**|Causal|**0.0915**|\n|Cauvis (Bottom-16 SVs)|Spurious|0.0006|\n|**Cauvis (Top-16 SVs)**|Causal|**0.2278**|\n|Cauvis (Bottom-32 SVs)|Spurious|0.0013|\n|**Cauvis (Top-32 SVs)**|Causal|**0.3022**|\n\nAnalysis of feature components from top and bottom singular values reveals a striking difference. Top-value features consistently target the foreground object, whereas bottom-value features scatter across the background. For instance, with k=3, the attention disparity is **over 915x**, which validates our hypothesis that high-energy components represent causal features and low-energy ones are spurious.\n\n3\\) To answer directly: **k is not a hyperparameter that requires tuning**. It is a theoretical concept for analyzing our model's output, not an input that controls its operation.\n\nOur method is designed to automatically learn a sharp separation in the feature energy spectrum, creating a large gap between a few high-energy \"causal\" components and a long tail of low-energy \"spurious\" ones. The FAR experiment confirms this, revealing a stark energy gap (915x for k=3) between the causal and spurious components. This separation is so robust that any reasonable k used for analysis successfully isolates the same causal features.\n\nIn short, the model learns the separation, not a specific value of k. This eliminates the need for manual k-selection, fully resolving the hyperparameter concern.\n|Feature Source|Assumed Role|FAR↑|\n|-|-|-|\n|Top-k SVs|Causal|High|\n|Bottom-k SVs|Spurious|~0 (Negligible)|\n\n> W4: Figure 1 and 4\n\nThank you for pointing this out. We will revise Figures 1 and 4 in our revised manuscript to improve clarity using larger fonts and thicker, high-contrast lines.\n\n>  Minor and Typos, grammartical errors\n\nWe sincerely thank the reviewer for their meticulous reading and valuable suggestions for improving the paper's clarity. We will incorporate all these changes in our revised manuscript. Specifically, we will add a brief explanation for the `do(X)` operator as suggested, and we will correct all noted typos while conducting a thorough proofread of the entire manuscript.\n\n> Q1: What does p in eq. (1) represent?\n\n`p` is the learnable prompt, and `p*` is its optimal value post-training. The subscript $|_{p=p*}$ in Eq. (1) means \"evaluated at this learned optimal state.\" This describes a training outcome, not a pre-supposed condition. We will clarify these definitions in the revision.\n\n> Q2: L199.\n\nThe equality in Eq. (7) represents the ideal theoretical goal of our optimization. It is intended to hold strictly under the condition that our model's representation (the right side) has perfectly learned to model the true interventional quantity (the left side). Our use of \"is equivalent to\" (L199) referred to this idealized outcome. We will revise the text to explicitly state the theoretical conditions for this equality, clarifying it as the target of our optimization. Thank you for helping us improve the paper's precision.\n\n> Q3: L202-203.\n\nTo be precise, the suppression of confounding effects is achieved by the cross-attention between prompts and image features, not by the prompts acting alone. The prompts simply provide the \"directions\" for this mechanism. We acknowledge the description in L202-203 was a simplification and will revise it for clarity. Thank you.\n\n> Q4: L220.\n\nWe apologize for the ambiguity. You are correct about L220: we meant the visual prompts after the Cross-Attention Prompts, not the prompt parameter `p` itself. We will revise the text to make this distinction explicit.\n\n> Q5: The 'w/o DINOv2' ablation.\n\nIn the 'w/o DINOv2' case, the backbone was a ResNet-50 (R50) pre-trained on ImageNet-1K, integrated within the DINO detector.\n\n[1] A fourier-based framework for domain generalization. CVPR 2021.\n\n[2]  Reducing domain gap by reducing style bias. CVPR 2021."}, {"review_id": "G1P8wXr6vu", "reviewer": "Reviewer_L5GS", "summary": "This paper focuses on Single-Domain Generalized Object Detection, training an detector on an annotated source domain and generalizing it to unseen domains. To increase the generalization ability, this paper proposes a cross-attention prompts to learn visual domain-invariant knowledge and a dual-branch adapter to disentangle causal-spurious features. Experiments verify the effectiveness of the proposed method. This paper uses the generalization of large visual models, combined with prompt tuning and adapter tuning, to design a framework for SDGOD task. Specifically, visual prompts are used to record domain-invariant knowledge, and adapters are used to decouple visual features. Experiments verify the effectiveness of the method.", "questions": "See weakness", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\nThe paper is easy to follow, the proposed method is intuitive, and the experiments are extensive.\nWeakness：\n1）Several success in applying prompt tuning and adapter tuning in domain adaptation have been made[1,2], which is similar to domain generalization task. Difference and commonalities should be claimed.\n2) The computational overhead should be discussed, as the usage of DINOv2 will introduce huge inference cost.\n3) The baseline performance is unclear. To prove the effectiveness of the proposed method, the relative improvement over the baseline should be considered. Furthermore, how does the proposed method perform on other baseline models? Portability should be an advantage of cue word tuning and adapter tuning.\n\n[1] Learning domain-aware detection head with prompt tuning. NeurIPS 2023\n[2] Da-ada: Learning domain-aware adapter for domain adaptive object detection. NeurIPS 2024", "was_revised": true, "final_justification": "I appreciate the authors' rebuttal, in which most of my concerns are addressed, so I decide to keep my positive score.", "rebuttal": "Thank you for your constructive review. We are very pleased that you found our paper easy to follow and our method intuitive. We also appreciate you recognizing that our experiments are extensive. We hope our detailed responses below can address your remaining questions.\n\n> W1: Domain adaptation works.\n\nClarifying the connections and distinctions between our work and these Domain Adaptation (DA) methods is crucial for accurately positioning our contribution. Below, we elaborate from two perspectives: technical commonalities and core differences.\n\nFirst, at the level of technical tools, our work shares commonalities with [1,2]. Both leverage Parameter-Efficient Fine-Tuning (PEFT) techniques (such as prompting and adapters) to modulate a powerful pre-trained model to address cross-domain visual recognition problems. This demonstrates the significant potential of PEFT techniques in handling domain shift issues.\n\nHowever, there is a fundamental difference in the core task setting and methodological innovation, which constitutes the main contribution of our work. Our work focuses on **Domain Generalization (DG)**, which aims to learn a model from a single source domain that can generalize to any unseen target domains. In contrast, [1,2] address **Domain Adaptation (DA)**, where the model can access unlabeled data from the target domain during the training/adaptation phase, with the goal of adapting to a specific target domain. Clearly, DG is a more challenging and more realistic scenario for real-world deployment, **as it assumes no prior knowledge of the target domain.**\n\nThis difference in task setting leads to our fundamental methodological innovation. The core idea of [1,2] is to use target domain data to learn \"domain-aware\" adapters, aiming to align the model's features with a specific target domain. In contrast, **Cauvis, using only source domain data, proactively learns to disentangle \"domain-invariant\" universal features** through our carefully designed prompts and dual-branch adapter. Our goal is not to adapt to any specific domain, but to enhance the model's core generalization ability to resist unknown domain shifts.\n\nTo ensure a fair comparison with Domain Adaptation (DA) works [1,2], we selected the highly challenging `Sim10k → Cityscapes` as our evaluation benchmark. This benchmark simulates a synthetic-to-real scenario and poses a rigorous test of a model's generalization capability due to the substantial domain gap between the source and target domains. The detailed experimental results are presented in the table below.\n|Model|S -> C|\n|-|-|\n|DA-Pro [1]|62.9|\n|SEEN-DA [3]|66.8|\n|DA-Ada [2]|67.3|\n|**Cauvis (Ours)**|**70.2**|\n\nAs can be seen, for generalization from synthetic to real scenarios, Cauvis still holds a 2.9% accuracy lead even without access to the target domain. \n\nThank you for your suggestion. We will add a discussion and comparison of these advanced DA methods in the \"Related Work\" section of our revised manuscript to more clearly define the contribution of our work.\n\n> W2: The computational overhead of Cauvis.\n\nWe greatly appreciate the reviewer's concern regarding computational efficiency. This is indeed a critical dimension for evaluating the practical utility of a method.\n\nWe acknowledge that choosing DINOv2 as the backbone introduces a significant base computational load. However, using powerful pre-trained models is a common and necessary practice in the current field to achieve State-of-the-Art (SOTA) performance and to ensure fair comparisons with other leading-edge works.\n\nThe core contribution of our work lies in the attached lightweight, parameter-efficient modules. Therefore, the key to evaluating our method's efficiency is the **additional overhead** introduced by these modules. In the field of Parameter-Efficient Fine-Tuning (PEFT), a crucial metric is the number of trainable/used parameters, as it directly reflects the computational components added during inference.\n\nTo quantify this overhead, we provide a comparison of parameter counts with other PEFT methods:\n|Model|Params|Avg. (%)|\n|-|-|-|\n|VPT|3.7M|59.0|\n|AdapterFormer|6.3M|57.8|\n|Rein|3.0M|59.0|\n|SoRA|4.9M|54.3|\n|**Cauvis (Ours)**|4.9M|**60.7**|\n\nAs the table shows, Cauvis (4.9M parameters) is highly competitive in parameter efficiency compared to the latest methods, and is even significantly more efficient than some, such as AdapterFormer (6.3M). More critically, the 4.9M parameters we introduce account for only about **1.6%** of the DINOv2 backbone's ~307M parameters. In a full forward pass, **the vast majority of the computation comes from the backbone network.** Therefore, the marginal inference cost introduced by our lightweight modules is minimal.\n\n> W3: The baseline performance is unclear, and evaluating portability.\n\nTo clearly demonstrate the effectiveness of our method, we first conducted rigorous baseline comparisons under two mainstream detection frameworks. The first is Faster R-CNN, where \"FR+DINOv2\" serves as the baseline. The second is the more advanced DETR-style detector, DINO, where \"w/o Cauvis\" (the detector without our module) serves as the baseline. This design is intended to comprehensively validate the beneficial effects of our method. The results are shown in Table 2 of the paper, as follows:\n|Model|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|\n|FR + DINOv2|50.6|59.0|43.4|55.4|52.1|\n|**FR + Cauvis**|**52.3**|**61.3**|**44.1**|**58.3**|**54.0**|\n|w/o Cauvis|50.5|57.0|42.2|54.8|51.1|\n|**Cauvis**|**56.5**|**64.6**|**47.6**|**61.2**|**57.8**|\n\nAs shown in the table above, our method brings significant performance improvements in both detection frameworks. In the Faster R-CNN, Cauvis yields a 1.9% average mAP gain. On the stronger DINO detector, the gain is even more substantial, reaching **6.7%**. These data clearly and quantitatively demonstrate that our proposed method can consistently improve the generalization ability of different detectors.\n\nBuilding on this, we fully agree that **portability is a core advantage of prompt/adapter tuning**. Another key validation in our work is demonstrating that Cauvis can serve as a universal module that seamlessly transfers to different Vision Foundation Models (VFMs). Table 6 of our paper provides exhaustive experiments on this, with the core results shown below:\n|Backbone|PEFT|DC|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|-|\n|EVA02-L|Freeze|63.0|45.2|48.6|27.3|38.8|44.6|\n|EVA02-L|**Cauvis**|**69.7**|**50.2**|**57.6**|**34.2**|**48.1**|**52.0**|\n|SAM-H|Freeze|69.7|50.5|52.5|28.8|52.9|50.9|\n|SAM-H|**Cauvis**|**72.2**|**53.7**|**55.8**|**31.9**|**55.7**|**53.8**|\n|DINOv2|Freeze|71.2|53.5|60.8|42.6|59.5|57.5|\n|DINOv2|**Cauvis**|**73.7**|**56.5**|**64.6**|**47.6**|**61.2**|**60.7**|\n\nUsing the \"Freeze\" method as a baseline, our method yields a **16.6% relative improvement** on EVA02, a **5.7%** improvement on SAM, and a **3.8%** gain on DINOv2. These consistent and significant performance gains across multiple mainstream backbones strongly demonstrate the excellent portability and universality of our method.\n\nThank you for pushing us to clarify these crucial aspects. We hope our detailed response and experiments have not only made the strong baselines and relative improvements clear, but have also turned the question of portability into one of our paper's most compelling strengths. The evidence is now clear: Cauvis is not a specialized solution for one architecture. By demonstrating significant relative improvements—**up to 16.6%**—on diverse backbones like EVA02, SAM, and DINOV2, we confirm it functions as a **truly universal enhancement module**. We believe this comprehensive validation strongly addresses your concerns and highlights the practical value of our work.\n\n[1] Learning domain-aware detection head with prompt tuning. NeurIPS 2023\n\n[2] Da-ada: Learning domain-aware adapter for domain adaptive object detection. NeurIPS 2024\n\n[3] SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection. CVPR 2025"}, {"review_id": "xLBjH15oTM", "reviewer": "Reviewer_soZz", "summary": "This paper tackles single-source domain generalization for object detection by enhancing performance on unseen target domains without accessing target data. To address spurious correlations, the authors introduce Cauvis, which combines Cross-Attention Prompts and a Dual-Branch Adapter. The Cross-Attention Prompt module injects learnable prompt tokens into every layer of a frozen DINOv2 backbone via cross-attention, effectively performing a backdoor-adjustment operation that suppresses non-causal features. The Dual-Branch Adapter then disentangles invariant causal representations from domain-specific noise using a causal MLP branch that maps prompts to spatially focused causal features and an auxiliary Fourier branch that extracts and filters frequency-domain confounders; these two streams are fused for robust detection. Extensive experiments on the SDGOD and Cityscapes-C datasets prove that Cauvis achieves state-of-the-art performance on both datasets.", "questions": "1. Could you please elaborate on why you fixed training to 12 epochs? In domain generalization studies, it is often more appropriate to select the checkpoint that achieves the best performance on the source domain rather than using a predetermined epoch. How would choosing the best source‐domain checkpoint instead of epoch 12 affect your results?\n\n2. Did you conduct experiments using harsh-weather conditions as the source domain, and if so, does the proposed method yield similar performance improvements under those settings?", "limitations": "Yes.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The paper is concise, well structured, and easy to follow, with clear motivation and explanations.\n2. The proposed method demonstrates substantial generalization gains (up to +31.4% mAP on SDGOD) while freezing the backbone, significantly reducing training compute.\n\n\nWeaknesses:\n1. While training cost is low, the added prompt and dual-branch layers likely introduce nontrivial inference overhead that is not quantified.\n2. Cross-dataset generalization is only measured on Cityscapes-C; testing on other corruption benchmarks (e.g., BDD100K-C, Foggy Cityscapes) would better validate the method’s applicability to diverse real-world scenarios.\n3. No experiments investigate whether the method holds when harsh-weather conditions are used as the source domain, leaving the generality of the approach under different source settings unverified.\n4. Hyperparameter choices for prompt length and adapter architecture are not thoroughly analyzed; the paper lacks a sensitivity study to guide practical selection.", "was_revised": true, "final_justification": "The authors have addressed my primary concerns on inference overhead, additional corruption benchmarks, and harsh-weather source experiments. As these issues are now satisfactorily resolved, I recommend Borderline Accept.", "rebuttal": "Thank you for your insightful review and for recognizing our paper's clarity (concise, easy to follow) and our method's high efficiency and substantial generalization gains. We have provided detailed responses to each of your points below. \n\n> W1: Training Cost.\n\nIn the field of Parameter-Efficient Fine-Tuning (PEFT), the standard practice for evaluating the overhead of a new module is primarily to measure the number of its additional trainable parameters. This is because for large foundation models like DINOv2, **the vast majority of the inference cost is dominated by the backbone itself.** The marginal latency added by lightweight PEFT modules is often too small to be reliably measured and can be heavily influenced by system-level optimizations. Therefore, **trainable parameters** serves as a more stable and direct metric for measuring the additional computational burden.\n\nTo quantify the overhead of our method using these standard metrics, we provide a comparison with other recent PEFT works.\n|Model|Params|Avg. (%)|\n|-|-|-|\n|VPT|3.7M|59.0|\n|AdapterFormer|6.3M|57.8|\n|Rein|3.0M|59.0|\n|SoRA|4.9M|54.3|\n|**Cauvis (Ours)**|4.9M|**60.7**|\n\nAs shown in the table, our method, Cauvis, is highly competitive in terms of parameter efficiency. With 4.9M parameters, it is on par with other state-of-the-art methods and is even more efficient than some approaches, such as AdapterFormer (6.3M). Crucially, these added parameters constitute only a tiny fraction (~**1.6%**) of the DINOv2 backbone's 307M total parameters. This quantitatively demonstrates that the inference overhead introduced by our prompt and dual-branch adapter is minimal, which aligns with the core philosophy of parameter-efficient tuning.\n\n> W2: Other corruption benchmarks.\n\nFollowing your valuable suggestion, we have conducted comprehensive new experiments on two additional benchmarks, Foggy Cityscapes and BDD100K-C.\n\nFirst, for the `Cityscapes -> Foggy Cityscapes` and `Sim10K -> Cityscapes` tasks, we used DINOv2 as the backbone and Faster R-CNN as the base detector for a fair comparison.\n|Model|Per.|Rid.|Car|Tru.|Bus|Train|Mot.|Bicy.|mAP|S->C|\n|-|-|-|-|-|-|-|-|-|-|-|\n|DINOv2|53.0|55.5|65.1|38.4|49.3|43.8|42.2|36.7|48.0|66.6|\n|**Cauvis**|**63.5**|**64.2**|**67.9**|**44.5**|**56.7**|**53.8**|**50.7**|**47.3**|**56.1**|**70.2**|\n\nOn the Cityscapes -> Foggy Cityscapes task (mAP column): As shown in the mAP column in the table above, compared to the strong DINOv2 baseline, our Cauvis method significantly boosts the mean Average Precision (mAP) from 48.0% to **56.1%**, achieving a substantial gain of **8.1%**. This clearly demonstrates that our method can effectively resist the domain shift caused by real-world weather conditions (like fog), enhancing the model's robustness.\n\nOn the Sim10K -> Cityscapes task (S->C column): This task is a more stringent and classic test of generalization capability, as it requires the model to generalize from synthetic data to a completely different real-world distribution. Under this setting, our method still achieves a significant performance improvement of **3.6%** (from 66.6% to **70.2%**). This indicates that our method can not only handle specific types of corruption but also learn domain-invariant essential features, thus successfully bridging the large gap between synthetic and reality.\n\nFurthermore, we conducted a comprehensive evaluation on the more challenging corruption benchmark, `BDD100K-C`, with the results shown below.\n|MODEL|Norm|Gua.|Shot|Imp.|Defo.|Glas.|Mot.|Zoom|Snow|Fros.|Fog.|Brig.|Cont.|Elas.|Pix.|JPEG.|mPC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|FR + DINOv2|53.2|33.4|35.1|32.3|42.5|38.5|41.3|20.7|39.4|38.3|51.3|51.0|51.1|49.1|48.4|47.3|41.3|\n|**FR + Cauvis**|**55.3**|**34.4**|**36.5**|**33.3**|**44.3**|**41.4**|**43.1**|**21.6**|**41.1**|**42.3**|**54.8**|**53.9**|**54.0**|**51.2**|**51.3**|**50.4**|**43.6**|\n\nThis experiment clearly demonstrates that across 15 different types of real-world corruptions, our Cauvis method achieves consistent and significant performance improvements over the strong baseline (FR+DINOv2), boosting the mPC from 41.3% to **43.6%**. This strongly validates that our method can effectively enhance the model's robustness in diverse, real-world scenarios.\n\n> W3: harsh-weather conditions are used as source domain.\n\nThank you for your valuable feedback. Considering that data collection for challenging scenarios is often difficult, the research paradigm of generalizing **from simple to adverse scenarios is more widely adopted.**\n\nTo thoroughly address this point, we have conducted three new sets of experiments. In these experiments, we use challenging conditions as the source domain and generalize to the clear-weather (`Day Clear`) target domain.\n\nIn the first generalization experiment (`Day Foggy -> Day Clear`), we used foggy weather as the source domain. As shown in the table, our method performed excellently, significantly boosting the mAP from 48.2% to **56.7%**.\n|Model|Bus|Bike|Car|Mot.|Per.|Rid.|Tru.|mAP|\n|-|-|-|-|-|-|-|-|-|\n|DINOv2|43.0|39.6|74.1|40.5|50.6|57.0|33.0|48.2|\n|**Cauvis**|**48.6**|**47.8**|**82.0**|**50.4**|**62.9**|**65.6**|**39.6**|**56.7**|\n\nIn the `Dusk Rainy -> Day Clear` task, our method improves the mAP from 47.0% to **58.9%**, once again validating its strong performance for generalization in the \"hard-to-easy\" direction.\n|Model|Bus|Bike|Car|Mot.|Per.|Rid.|Tru.|mAP|\n|-|-|-|-|-|-|-|-|-|\n|DINOv2|59.6|24.5|80.1|33.5|39.2|40.1|51.9|47.0|\n|**Cauvis**|**67.9**|**39.9**|**86.1**|**45.6**|**57.1**|**55.3**|**60.4**|**58.9**|\n\nFinally, we tested the generalization from `Night Clear -> Day Clear`. Our method once again provides a clear advantage, raising the mAP from 63.7% to **66.1%**.\n|Model|Bus|Bike|Car|Mot.|Per.|Rid.|Tru.|mAP|\n|-|-|-|-|-|-|-|-|-|\n|DINOv2|68.7|50.5|85.8|51.1|64.1|65.4|60.4|63.7|\n|**Cauvis**|**69.9**|**54.0**|**86.9**|**53.4**|**70.1**|**66.2**|**62.5**|**66.1**|\n\nOur method's generalization is robust across diverse challenges (harsh weather) because it is not tied to a specific difficulty type. It effectively disentangles essential object features from spurious domain correlations (e.g., fog, rain), ensuring strong performance in varied scenarios.\n\n> W4: Hyperparameter choices for prompt length and adapter.\n\n**Regarding the prompt length,** we would like to clarify that the relevant analysis is already included in Figure 4 of our paper. To present the results more clearly in this response, we provide the data in the table below. The study shows that performance does not significantly improve as the length increases; however, the best performance is achieved when the length is aligned with the sequence ($L_{seq}$).\n|Prompt length|DC|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|\n|50|73.3|55.9|61.8|46.9|59.7|59.5|\n|100|72.6|55.9|63.3|47.5|59.9|59.8|\n|150|73.0|55.6|62.0|47.2|60.3|59.6|\n|200|73.0|56.1|62.3|46.8|60.4|59.7|\n|$L_{seq}$|73.5|56.0|62.5|47.8|59.9|**59.9**|\n\n**For our dual-branch adapter**, its design does not introduce complex new hyperparameters that require tuning. Its effectiveness comes from its specific architectural components. Therefore, to justify our design, we conducted comprehensive ablation studies by replacing the core components with other common and reasonable alternatives. The following experimental data all come from Table 4 of the paper.\n\na) Causal Branch. We evaluated the effect of replacing our simple causal mapping function with more complex alternatives. The results below show that our current design achieves superior or highly competitive performance, validating that increasing the complexity of this branch is not beneficial.\n|Function|DC|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|\n|Multihead|72.8|55.2|62.2|46.4|60.6|59.4|\n|Seblock|71.4|54.9|62.4|47.0|58.9|58.9|\n|Gate Unit|72.9|55.3|62.1|46.0|59.1|59.0|\n|3$\\times$3 Conv|72.2|54.0|61.9|46.9|58.1|58.6|\n\nb) Auxiliary Branch. We also validated the design of our auxiliary branch (based on FFT). The results show that our current implementation, which performs implicit regularization through the Fourier domain, is more effective than explicitly adding a mask to the Fourier components or removing the branch entirely.\n|Function|DC|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|\n|Add Mask in FFT|73.7|56.2|64.1|48.2|60.1|60.5|\n|w/o FFT|73.0|55.0|62.9|48.5|59.3|59.8|\n\nThese extensive experimental studies confirm that our method is robust to the choice of prompt length, and that our Adapter is a well-justified design, validated through comparisons with multiple alternatives. We will ensure these justifications are more prominent in the revised manuscript.\n> Q1: training to 12 epochs\n\nWe chose a fixed 12-epoch training schedule to strictly follow the standard baseline protocol (e.g., the 1x schedule for Faster R-CNN) to ensure a fair comparison. This ensures that our performance gains stem from the architectural design, rather than from special adjustments to the number of training epochs.\n\nFurthermore, we fully agree that selecting the \"best source-domain checkpoint\" is a standard practice in Domain Generalization (DG). We analyzed the performance of each epoch (see table below)\n|Epochs|DC|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|\n|1|48.8|36.9|41.2|31.1|39.7|39.5|\n|2|61.3|46.5|51.8|40.7|50.7|50.2|\n|3|63.6|47.4|53.8|40.5|50.7|51.2|\n|4|66.0|50.4|56.4|42.2|55.0|54.0|\n|5|70.5|53.1|59.2|45.0|57.3|57.0|\n|6|70.3|53.2|60.6|46.2|58.4|57.7|\n|7|70.9|53.3|60.6|47.1|57.9|58.0|\n|8|71.2|54.4|61.1|46.5|57.6|58.2|\n|9|71.5|54.0|62.0|45.8|58.1|58.3|\n|10|72.2|54.6|61.3|46.0|58.4|58.5|\n|11|72.3|55.1|63.4|45.2|59.4|59.1|\n|12|73.7|56.5|64.6|47.6|61.2|60.7|\n\nTherefore, even if we were to adopt the \"best source-domain checkpoint\" strategy, we would still select the model from epoch 12. This demonstrates that our currently reported results are robust and in line with best practices.\n\n> Q2:Using harsh-weather conditions as the source domain.\n\nFor the detailed results, tables, and a full analysis, please refer to our detailed response to **W3**."}, {"review_id": "yXILWn81Id", "reviewer": "Reviewer_pbD8", "summary": "This paper focuses on Single-Domain Generalized Object Detection, training an detector on an annotated source domain and generalizing it to unseen domains. To increase the generalization ability, this paper proposes a cross-attention prompts to learn visual domain-invariant knowledge and a dual-branch adapter to disentangle causal-spurious features. Experiments verify the effectiveness of the proposed method.", "questions": "Please refer to Weakness.", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1)The motivation is clear and reasonable.\n\n2)The paper is clearly organized and the figures and tables are easy to understand.\n\n3)The experiment is extensive.\n\nWeakness：\n\n1）The contribution is not clearly stated. In my opinion, the introduction of DINOv2 into SDGOD cannot be considered a contribution, and there are related works [1-3] that apply pre-trained large models to domain adaptation/domain generalization problems. The differences from these methods and the specific contributions need to be clarified.\n\n2）Fairness of experimental comparison. Introducing a large pre-trained model will obviously improve the generalization of the model. As shown in Tables 2 and 3, with the addition of DINOv2, the performance of the baseline method has greatly exceeded that of the existing algorithm, so this comparison is not fair.\n\n3）Since the proposed method uses prompt and adapter design, it is better to add an introduction to the related methods of prompt[1] and adapter[4] in domain adaptation/domain generalization in related work section.\n\n[1] Learning domain-aware detection head with prompt tuning. NeurIPS 2023\n\n[2] Domain adaptation via prompt learning. TNNLS 2023\n\n[3] SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection. CVPR 2025\n\n[4] Da-ada: Learning domain-aware adapter for domain adaptive object detection. NeurIPS 2024", "was_revised": true, "final_justification": "Thanks for the author's' detailed rebuttal. Most of my concerns have been addressed, so I maintain my original positive score.\n\nHowever, I still believe that the introduction of DINOv2 into SDGOD cannot be considered a contribution. While the DA task and the DG task have different priors, the core idea of leveraging the high generalization knowledge of the VLM remains unchanged. Therefore, I suggest that the authors include a discussion of the VLM's impact, especially on the DG task, in the manuscript, and supplement all the discussion in the rebuttal period for better illustration.", "rebuttal": "We thank you for your constructive feedback and positive assessment of our work. We greatly appreciate your recognition of our clear and reasonable motivation, the clarity of the paper's organization and figures, and the extensive nature of our experiments. We hope our detailed responses below can address your remaining questions.\n\n> W1: Contributions are unclear and ask for clarification of how our approach differs from and innovates beyond existing methods.\n\nWe believe that our previous description may not have sufficiently highlighted the fundamental differences between our work and [1-3], which may have led to a misunderstanding of our contribution.\n\nFirst, the core setting of our work is **Single-Domain Generalization for Object Detection (SDGOD)**, which is fundamentally different in its problem definition from the **Domain Adaptation (DA)** field to which the cited references [1-4] belong. The core premise of DA methods is access to unlabeled target domain data during training for distribution alignment. In contrast, the SDGOD setting is far more stringent, with **absolutely no access to any target domain data during training**. It requires the model to learn solely from a single source domain and generalize to any unseen domains. This key distinction means that methods designed for DA, which rely on target domain data, cannot be directly applied to SDGOD.\n\nSecond, our method's **design philosophy** is also fundamentally different from these works. Unlike the text prompts used in works [1-3]—where text inherently carries a domain-invariant prior due to its semantic abstraction—our visual prompts do not rely on any external modality and are learned **\"from scratch.\"** This forces the model to actively mine generalizable knowledge solely from the single source domain. Regarding work [4] (DA-Ada), the core of its adapter design is to learn source- and target-specific knowledge separately, which relies on accessing unlabeled target data during training. In contrast, our dual-branch adapter operates under the SDGOD setting with no visibility of the target domain, and its goal is to identify and suppress source-specific, potentially harmful spurious features. Therefore, our work is conducted under a much more challenging setting.\n\nTo illustrate the point, our method was compared with several advanced Domain Adaptation (DA) methods. On the `Cityscapes -> Foggy Cityscapes` and `Sim10K -> Cityscapes` tasks, the results are shown in the table below:\n|Model|Per.|Rid.|Car|Tru.|Bus|Train|Mot.|Bicy.|mAP|S -> C|\n|-|-|-|-|-|-|-|-|-|-|-|\n|DA-Pro [1]|55.4|62.9|70.9|40.3|63.4|54.0|42.3|58.0|55.9|62.9|\n|SEEN-DA [3]|58.5|64.5|71.7|42.0|61.2|54.8|47.1|59.9|57.5|66.8|\n|DA-Ada [4]|57.8|65.1|71.3|43.1|64.0|58.6|48.8|58.7|58.5|67.3|\n|FR + DINOv2|53.0|55.5|65.1|38.4|49.3|43.8|42.2|36.7|48.0|66.6|\n|FR + Cauvis|63.5|64.2|67.9|44.5|56.7|53.8|50.7|47.3|56.1|**70.2**|\n\nIt is important to emphasize that **DA methods have access to unlabeled data from the target domain (Foggy Cityscapes) during training**, which gives them a natural advantage. Therefore, while our method shows significant improvement over the strong baseline (from 48.0% to **56.1**), its performance is comparable to some of the DA methods.\n\nHowever, the true test of generalization capability lies in scenarios with a large domain gap between the source and target domains. The **Sim10K -> Cityscapes task (S->C column)** represents such a stringent and realistic setting, where the source domain consists of **synthetic data** and the target domain is **real-world imagery**. In this setting, our Cauvis method significantly outperforms all Domain Adaptation (DA) methods, with a lead of **2.9%** over the best-performing method, DA-Ada [4]. This strongly demonstrates the superior generalization capability of our method in the **\"synthetic-to-real\"** scenario.\n\nBased on this, we would like to directly address your concern that \"the contribution of introducing DINOv2 is not strong.\" In the highly challenging field of SDGOD, exploring and leveraging more powerful Vision Foundation Models (VFMs) has become a recognized trend and a core competitive point for pushing the state of the art and breaking through performance bottlenecks. For instance, prior work such as CLIP the Gap [5] explored the potential of CLIP, while UFR [6] incorporated SAM to generate masks for extracting domain-invariant representations. This indicates that **the choice of VFM and how to leverage it is a core research question in this domain**.\n\nOur primary contribution, therefore, is that through systematic research and experimentation, we identify **DINOv2 as a more suitable foundation model for the SDGOD task** than other VFMs used in previous works (like CLIP and SAM). Our experimental results (as shown in the table below) overwhelmingly outperform prior methods based on other VFMs, which strongly validates the correctness and forward-thinking nature of our technical approach.\n|Model|Backbone|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|-|\n|ClipGap [5]|CLIP|38.6|32.3|18.7|36.9|31.6|\n|UFR [6]|SAM|39.6|33.2|19.2|40.8|33.2|\n|SoRA [8]|DINOv2|51.0|59.3|47.6|59.3|54.3|\n|Rein [7]|DINOv2|55.0|62.4|45.2|59.4|55.5|\n|**Cauvis (Ours)**|DINOv2|**56.5**|**64.6**|**47.6**|**61.2**|**57.8**|\n\n> W2: Performance of the baseline method has greatly exceeded that of the existing algorithm, because used the DINOv2.\n\nFirst, we believe that in the era of Foundation Models, exploring and leveraging more powerful pre-trained models like DINOv2 to push the performance ceiling of the highly challenging SDGOD task is an inevitable direction for the field's development. We consider the successful identification and validation of DINOv2's significant potential for the SDGOD task to be an important contribution of our work in itself, as it provides a higher and more challenging starting point for subsequent research.\n\nWe fully agree that the most ideal way to compare would be to integrate DINOv2 as the backbone into all prior SOTA methods (like UFR, Clip the Gap) and then conduct a fair comparison. However, in practice, we encountered an insurmountable obstacle: **these key SOTA works have not released their official source code.** This made it impossible for us to replace the backbone network within their frameworks to conduct a rigorous comparison, which is a practical dilemma within the field.\n\nTherefore, to validate the effectiveness of our method (Cauvis) in the fairest and most rigorous manner, we adopted what we believe to be the most transparent and scientific alternative: we first combined DINOv2 with a standard Faster R-CNN detector to create the **\"Faster R-CNN + DINOv2\"** baseline. The performance of this baseline itself, as you noted, is already very strong. The core of our experiment, therefore, is to benchmark our method, Cauvis, against this powerful new baseline. For clarity, Table 2 and 3 from our paper is shown again below, compared to \"FR + DINOv2,\" our Cauvis method still achieves significant performance improvements (e.g., a **2.9%** increase on Night Clear, **2.3%** on Dusk Rainy, **1.7%** on Day Foggy and **6.3%** on Cityscapes-C).\n|Model|DF|DR|NR|NC|Avg.|\n|-|-|-|-|-|-|\n|FR + DINOv2|50.6|59.0|43.4|55.4|52.1|\n|**FR + Cauvis**|**52.3**|**61.3**|**44.1**|**58.3**|**54.0**|\n\n|Model|Gua.|Shot|Imp.|Defo.|Glas.|Mot.|Zoom|Snow|Fros.|Fog.|Brig.|Cont.|Elas.|Pix.|JPEG.|mPC|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|FR + DINOv2|14.5|16.3|13.5|33.9|27.3|33.1|13.7|25.2|31.5|39.5|42.5|38.9|42.1|35.9|31.8|29.3|\n|**FR + Cauvis**|16.8|19.8|15.2|41.4|34.0|39.2|15.8|29.8|36.7|48.8|53.0|49.5|52.0|43.9|38.8|**35.6**|\n\n> W3: More proposed methods that use prompt and adapter designs should be discussed.\n\nWe thank you for your valuable suggestion. As detailed in our response to **Weakness #1**, our work operates under the more challenging Single-Domain Generalization (SDGOD) setting, which is fundamentally different from the Domain Adaptation (DA) methods in [1, 4]. It is precisely this difference in setting that leads to the fundamental innovation in our methodology. For example, unlike [1], our approach does not rely on the inherent semantic invariance of text prompts. Instead, it requires our visual prompts to learn entirely \"from scratch,\" actively mining generalizable knowledge from the single source domain without any information about the target domain. Similarly, in contrast to DA method [4], which learns specialized knowledge for specific domains, our goal is the opposite: to actively discard source-specific spurious features to achieve generalization to any unseen domain, all without ever seeing the target domain. This ultimately highlights the fundamental difference in philosophy between our work and these methods—we pursue **\"generalization\"** rather than **\"adaptation.\"**\n\nFor a direct comparison, we conducted a synthetic-to-real generalization experiment (Sim10k->Cityscapes) to benchmark against DA works, with results shown in Table **W1**. Even without any access to target domain data, our method achieves a **2.9** improvement over [4], demonstrating its strong generalization capability.\n\nWe will add a discussion of these DA methods and make corresponding revisions in our updated manuscript.\n\n[1] Learning domain-aware detection head with prompt tuning. NeurIPS 2023\n\n[2] Domain adaptation via prompt learning. TNNLS 2023\n\n[3] SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection. CVPR 2025\n\n[4] Da-ada: Learning domain-aware adapter for domain adaptive object detection. NeurIPS 2024\n\n[5] Clip the gap: A single domain generalization approach for object detection. CVPR 2023\n\n[6] Unbiased faster r-cnn for single-source domain generalized object detection. CVPR 2024\n\n[7] Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation. CVPR 2024.\n\n[8] SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning. CVPR 2025."}]

Author rebuttal:

Meta-review: This paper introduces Cauvis, a method for Single-Source Domain Generalized Object Detection (SDGOD) that tackles the critical challenge of spurious correlations. The core of its contribution lies in using a cross-attention prompt module, inspired by causal inference, and a dual-branch adapter to disentangle domain-invariant causal features from spurious ones. The strengths of this work, as highlighted by the reviewers, include its clear motivation, sound theoretical rationale, and extensive experiments demonstrating state-of-the-art performance with significant gains over existing methods. Initial weaknesses and questions were raised by the reviewers, including concerns about the justification for the dual-branch adapter's design (vcyR), the fairness of comparisons given the strong DINOv2 backbone (pbD8), computational overhead (L5GS), and the need for more extensive validation on diverse benchmarks and source domains (soZz). The authors provided a comprehensive and convincing rebuttal, effectively addressing all major points. They clarified the training procedure, provided new quantitative experiments to validate the disentanglement of features, and engaged in an in-depth discussion that ultimately clarified the role and mechanism of the Fourier transform component in the adapter, satisfying reviewer vcyR. They also conducted new experiments on additional corruption benchmarks and with harsh-weather source domains, demonstrating the method's robustness and generality, which addressed the concerns of reviewer soZz. Furthermore, they successfully justified the performance gains over a very strong baseline and contextualized their contribution regarding the use of large foundation models, addressing the concerns of reviewers L5GS and pbD8. Based on the reviews and discussion, AC recommends accept.
Decision: Accept (poster)