CroPe: Cross-Modal Semantic Compensation Adaptation for All Adverse Scene Understanding
Authors: Qin Xu
Qihang Wu
Lu Hongtao
Xiaoxia Cheng
Bo Jiang
Venue: NeurIPS 2025 poster ()
Keywords: cross-domain semantic segmentation; unsupervised domain adaptation; multi-modality
Primary area: applications

Abstract: Scene understanding in adverse conditions, such as fog, snow, and night, is challenging due to the visual appearance degeneration. In this context, we propose a Cross-modal Semantic Compensation Adaptation method (CroPe) for scene understanding. Distinct from the existing methods, which only use the visual information to learn the domain-invariant features, CroPe establishes a visual-textual paradigm which provides textual semantic compensation for visual features, enabling the model to learn more consistent representations. We propose the Complementary Perceptual Text Generation (CPTG) module which generates a set of multi-level complementary-perceptive text embeddings incorporating both generalization and domain awareness. To achieve cross-modal semantic compensation, the Reverse Chain Text-Visual Fusion (RCTVF) module is developed. By the unified attention and reverse decoding chain, compensation information is successively fused to the visual features from the deep (semantic dense) to shallow (semantic sparse) features, maximizing compensation gain. CroPe yields competitive results under all adverse conditions and significantly improves the state-of-the-art performance by 6.5 mIoU for ACDC-Night dataset and 1.2 mIoU for ACDC-All dataset, respectively.

CroPe: Cross-Modal Semantic Compensation Adaptation for All Adverse Scene Understanding
Qin Xu Qihang Wul.2, Hongtao Lu Xiaoxia Cheng 3, Bo Jiang 1:2+
School of Computer Science & Technology, Anhui University
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University
*College of Computer Science & Technology. Zhejiang University
suqin@ahu.edus edu. cn, {e23301339, e24301204) ahu edu.
zjucxxxzju edu. cn, zeyiabc@163. com
Project website: https: http://gghbbu..c//////11222333/
com comm/woh011122///rrrr
Abstract
Scene understanding in adverse conditions, such fog, snow, and night, is chal- lenging due to the visual appearance degeneration. In this context, we propose a Cross-modal Semantic Compensation Adaptation method (CroPe) for scene under- standing. Distinct from the existing methods, which only use the visual information to learn the domain- invariint features, CroPe establishes visual-textual paradigm which provides textual semantic compensation for visual features, enabling the model learn more consistent representations. We propose the Complementary Perceptual Text Generation (CPTG) module which generates a set of multi-level cumplementary-pereptiiee text embeddings incorporating both generalization and domain awareness. To achieve moss-modal semantic compensation, the Reverse Chain Text- Visual Fusion (RCTVF) module is developed. By the unified attention and reverse decoding chain, compensation information is successively fused to the visual features from the deep (semantic dense) shallow (semantic sparse) features, maximizing compensation gain. CroPe yields competitive results under all adverse conditions and significantly improves the state-of-the-ar performance by 6.5 mloU for ACDC-Night dataset and 1.2 mloU for ACDC-All dataset, respectively.
1 Introduction
Scene understanding under adverse weather conditions serves essential task for outdoor applica- tions, such as autonomous driving, surveillance systems, and disaster response. However, due the extreme changes in illumination, texture, and occlusion patterns under various adverse conditions, the large domain discrepancies across diverse scenes pose a significant challenge, making it difficult for existing methods to effectively address segmentation under all adverse weather conditions.
Existing methods can be divided into two groups. One scene-specific framework [1, 3. 5, 6), which is tailored to particular adverse conditions. For example, BWG [3] enhances the generalization ability for foggy scenes through content enhancement and style decorrelation. S2R2 [2] jointly optimizes deraining and segmentation tasks using contrastive learning. Despite the successes in certain scenarios, the model's 's performance declines when confronted with more complex and diverse scenes. Another group is scene-agnostic framework [7, 8, 9, 10, 11, 12] which offers more unified solution. For instance, PASS [13) utilizes an implicit visual prompt strategy to enhance cross-domain consistency by eliminating domain-specific weather features. MIC [9] captures contextual information of the scene through mask reconstruction. However, whether scene-specific or scene-agnostic
Corresponning authors: Xiaoxia Cheng and Bo Jiang
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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eriual prompt?
ACDC-Night
mark
hallucination
ND
Tagget Image
Visual Modeling
Truining images: domain
0.4
nighttime
0.2
whyy
BD
sky
Ground Truth
ACDC Rein
ACOC
(1) Existing framework
PASS
(h) Performance Comparison
Reverse Chain
Text-Visual Fusion
Pred
Taggel
Learnable
Complementary- Perccpiiiveeee
Handerafted
Text Generation
Ground Truth
Text Prompes
Our proposed framework: CroPe
(a) Segmentation Paradigm Comparison
(c) Visualization Comparison
Figure I: Comparison between CroPe and existing methods: (a) In contrast existing methods relying solely on visual modality, our CroPe integrates visual and text modality. (b) Our CroPe outperforms existing methods in many adverse scenarios. (c) Existing methods often produce hallucinations (e.g., incorrectly classifying the sky as trees). We highlight the differences between our CroPe and existing methods using dashed boxes, and zoom in with solid boxes for emphasis.
frameworks, they only train the model within the visual modality (e.g. using visual prompts, mask reconstruction or tuning) to Iearn fomain-invariany knowledge for unsupervised domain adaptation (UDA) segmentation, as shown Figure 1 (I) Under adverse conditions, visual features often experience severe degradation (c.g., loss of texture, low visibility, and color distortion). This degradation makes the model prone hallucinations and complicates the learning of consistent semantic knowledge across different domains.
To address the aforementioned issue, introduce the integration of robust text modality into unsu- pervised domain adaptive semantic segmentation (UDASS) under adverse conditions and propose novel cross-modal semantic compensation adaptation method (CroPe) for all adverse scene derstanding. Since text semantics are invariant to environmental changes and facilitate acquisition of across-domain class-consistent semantics, our CroPe leverages semantics as high-level guidance modality to compensate for degraded visual information under adverse conditions, which can effectively obtain semantic consistency across different domains, illustrated Figure (a)(2). wtapl/mmenttr--Preeppttii Text Generation (CPTG), which is composed of a decoupling strategy, domain-specinf perception, domain- invariinn regularization, and gated complementary fusion. The decoupling strategy is proposed to decouple the text embeddings into domain invariant embedding which is constrained by domain- invariint regularization and domain perceptive embedding which is interacted with visual features by the domain-specifer perception. The gated complementary fusion designed to adaptively fuse the domain-specific and domain-invarian text embeddings. After the CPTG module, the Reverse Chain Text- Visual Fusion (RCTVF) module is designed, which develops the unified attention and reverse decoding chain. The unified attention mechanism integrates the multi-scale visual features and the multi-level textual features outputted by CPTG. The reverse decoding chain incorporates the visual features compensated with deep se- mantics into the shallower, yet unfused, visual feature. By this chained fusion, compensation gain of visual features can be maximized. CroPe surpasses most of existing visual frameworks, effectively addressing visual degradation in UDASS, avoids erroneous classifications such mistaking skies for roads. The performance superiorities of CroPe in comparison with the SOTA methods on ten datasets under challenging scenarios is shown in Figure (b). The segmentation maps presented in Figure (c) qualitatively illustrate CroPe's adaptability in adverse scenarios.
Specifically, we propose the
Our contributions are briefly summarized as follows:

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We propose cross modsll semantic compensation method, which integrates the textual modality into the unsupervised domain adaptation semantic segmentation task under adverse scenes to enhance the model's ss adaptability.
We design CPTG module to generate multi-level which are then integrated into visual features using the RCTVF module achieve cross- modal semantic compensation.
text embeddings,
Extensive experimental results show that our method achieves state-of-the-ar performance in various adverse scenarios, including rain, snow, fog, and nighttime, while also reducing training cost. This highlights the model's superiority in both effectiveness and efficiency.
2
Related Work
Adverse visual scenes hinder effective knowledge transfer in unsupervised domain adaptation (UDA) for scene understanding. Early studies primarily focus single scenarios. For example, FIFO [14] focuses on fog scenes and learns fog-invariant representations by extracting fog-related factors from style features. Some works focus on night scenes [15, 16], using -uperssupeiisioon through day-night paired images or cross-temporal correspondences. These methods perform well in spe- cific scenes but struggle generalize across diverse adverse conditions. Recent research turns to developing a unified framework capable of handling multiple adverse scenes simultaneously [17, 13]. For example, Refign [18] introduces an uncertainty-aware dense matching method align the target image and the reference image from various adverse scenes, thereby improving its robustness across multiple adverse scenes. DAFormer [7] further improves the expressiveness various scenarios by introducing training strategies such as Transformer encoder, rare category sampling, and ImageNet feature distance constraints. SePiCo [19] proposes a semantically guided pixel comparison method, which constructs a cross domainn discriminative embedding space through center aware and distribution-avv aware comparison losses, and simultaneously optimizes feature alignment and self- training stability. The unified framework has become the mainstream solution due to its powerful cross-scenario capabilities. However, existing unified framework methods rely on visual modeling to capture domain invariance and struggle to address the challenges posed by significant visual distortion. In this paper, we introduce the first cross-modal semantic compensation method to learn domain- invariant features for UDA. More related works on UDA semantic segmentation can be found in Appendix A.I.
Method
In this section, we first give the task formulation of unsupervised domain adaptation (UDA) scene understanding in adverse scenes in $3.1 and then describe our method in detail. As shown in Figure our proposed CroPe consists of two components, a Complementayy-Prreepiiee Text Generation $3.2 and a Reverse Chain Text- Visual Fusion $3.3.
3.1 Task Formulation
R3 represents the input images the training set from the source domain S and the target domain T,, and y" E IR HW is the corresponding image label from the source domain, H and W represent the resolution. The goal of the UDA scene understanding task is to use 1s, yS. and I" for training model with good segmentation performance in the target domain test set.
Given training sample (I$,1",y²) where (1)
3.2 Complementary-PPeeeppe Text Generation
The Complementary- Percepptie Text Generation (CPTG) aims to obtain set of multi-level complementary perceptive text embeddings provide more effective completion and alignment for cross-modal semantic compensation from textual to visual modality. A key challenge of CPTG is the design of textual prompts, as using learnable or hand-crafted text prompts alone poses the risks of overfitting and limited flexibility, respectively. Therefore, we propose use learnable prompts as the core and combine them with hand-crafted prompts in the Comain-Invarian Regularization to complement each other.
3

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Reverse Chain Text-Visual Fusion (RCTVF)
Reverse Decoding
t.. Outpot
Comperl
Prozen
Unifed Atlestion
(Pseudoo
MMS Defeem Alm
Cross Art
Add & nomm
add & svors
Ria Bog
Las
Learnable
FRN
Semantic Compensation
Matrix Mul
XN
2%
Pred
Stop Gradiens
Complementary-Pereee Text Generation (CPTG)
Commain-Sprifffe Perception
Gated Complementary Fasion
Domain Invariat Regularisation
FPN
Transformer Decoder
Cos Distance
Lalign
x
10
Tame
Vistaa
PT.TH.23
Gating
Transformer Decoder
y
K
MLPS MLS
Tc
Test
Transformer Decoder
Lorth
Fmonddr
Decouple
Prompt.Human Crafied)
Input Image
[V]
[V
MV das Text Prompt
Figure The overview of our proposed CroPe. The CPTG module receives image features [P2, Ps. Pal from the visual encoder and global text features T from the text encoder. It processes them through Domain- Speciffc Perception, Domain Invariant Regularization, and Gated Comple- mentary Fusion to generate multi-level
ceetemeenttar-eereppii text embeddings T [T. T2, Ta.. Next, the RCTVF module fuses into the multi-scale visual features [F1, F2. F3, Fal through Uni- fied Attention and Reverse Decoding Chain, outputs the multi-scale semantic compensated visual features Q [Qi, Q2, Q3, Qi), which are then passed into the segmentation head to obtain the final segmentation predictions.
Specifically, we first input the learnable text prompt of each category [V][[V] 2. [V]M [class] into the text encoder to generate the corresponding raw text embeddings 7 E IR R where V, [class]. M. C. and denote the context word, category name, the number context words, the number of categories, and the dimension of text embedding, respectively.
Decoupling Strategy. Instead of direct optimization of T, propose decoupling strategy to learn two independent structures of textual prompts, i.e., generality and domain awareness. Specifically, we use two identical multi-layer perceptrons (MLPs) (with non-shared weights) decouple T into the domain-specific embedding T's E RCD and the domain-invariant embedding T' E RCDD Meanwhile, ensure clear semantic independence between these decoupled feature embeddings, we apply an orthogonality constraint on Ts and T,. The orthogonal loss is defined as follows:
Lorth T?)1
(1)
where (...) represents the cosine similarity. By explicitly decoupling I's and T,, establish optimization objectives for domain invariance and domain perception independently.
Domain-Specifie Perception. To enhance the Comain-awareneee ability o's. we propose a domain- specific perception module where the correlation of Ts and the visual representation is explored and mined. To balance computational efficiency and the richness of domain-specine we use the last three level features [P2, Ps, Pa] from the visual encoder as the visual clues with the domain perception. Then, both Ts and [P2, Pa, P4] are projected from D to the channel dimension D& through linear layers, with Ts mapped to the query tokens QTg. and P. mapped to the key tokens Kr, and value tokens Vp, where i E (2, 3, 4). Through attention interactions, T' can capture multi-level specifc information, the process expressed as:
information,
QI: (Q=KKKK K
Qr = Qr;, Softmax
Vp..., i (1,2,3}
(2)
DDa
where 75 = Ts. Finally, each output Qr is restored from Da to D dimension through linear layer, obtaining T [TT T3. [7] 3x3xDD as the multi-level perception output. By aligning T's and

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the visual space, we obtain the domain- -omain-specific perceptive embedding T which captures the local and global context.
Domain-Invariant Regularization. To enable the domain-invariant embedding Ty to effectively generalize across different domains, utilize manually designed prompts that provide general representations and maintain invariance. Concretely, by creating a general text prompt and obtaining the corresponding text embedding Te IR C through the text encoder, we exploit a cosine similarity soft constraint Latign between T' and Tc, ensuring that I does not deviate excessively from Te, thereby maintaining the domain robustness. The soft constraint Lolign is defined follows:
Tralign
Tc),
By minimizing the Latign in Equation (3), we can both prevent 77 from overfitting specific domain and allow Ty to learn more generalized representations.
Gated Complementary Fusion. To generate aapplemenntry-pereppiiin text embedding, we propose invariant text embeddings
a gated complementary fusion that integrates domain-speeti specific and through dynamically adjusting the contribution of each embedding. Primarily, Gating Unit is designed. In this unit, Ty E RCCD broadcasted and concatenated with T E Rx3xxxD along the dimension of the feature as the input, and then passed through linear layer to obtain the fused feature Tusse E 3x Meanwhile, we fed the input into a single hidden layer multilayer perceptron (MLP) and Sigmoid activation function to learn spesifce gating weights {a, 1x for Ty and T fuse. Finally, we obtain the multi-level campleemettary-prreepe text embeddings T = T2 T2. T3] E R3xCxD
as followings:
î T= a Ti + B B-Teuee
The gated complementary fusion mechanism dynamically adjusts the fusion weights between domain perception and generalization information, enabling the complementary integration of the text semanties. This mechanism effectively enriches text cross-domain representations, providing a robust basis for enhancing the semantic density of visual features.
3.3 Reverse Chain Vext-Visual Fusion
To fully integrate multi-level complemeentary--prrcpppii text embedding semantics into the visual modality and achieve moosl- semantic compensation, we propose Reverse Chain Text- Visual Fusion (RCTVF) module. As shown in Figure 2 (Upper), RCTVF receives T generated by the CPTG module and the multi-scale features [Fi, F2, Fs, FA] generated by the FPN [20]. The resolution of
the multi-scale features is [ x, 8 x, T x, ø2 x] of (H,, respectively, RCTVF consists of Unified Attention and Reverse Decoding Chain components.
Unified Attention. The Unified Attention integrates multi-scale deformable attention [21] and cross- modal attention, allowing the model to concentrate on key areas various scales while maintaining text semantic guidance. Then, we fuse the channel information through a feedforward network (FFN) to enhance the semantic density of visual features.
Specifically, we input one of the scales of (i E (1,2,3,4)) into the Unified Attention module at a time, initially capturing both detail and global information via the multi-scale deformable attention. Next, we interact the output visual information with the corresponding T;-1 using the cross-modal attention, and then further integrate the cross-modal semantics through the FFN. We also map F; and f: to dimension Da create QF K and Vr,_, The specific calculations are as follows:
Qi = A
K T V), = 4,3,2.
Among them, the semantic compensated visual features (Q;, Q5. Qi] of each scale can be generated using Equation (5). Due QF, having the largest resolution, we control the computational complexity by processing QF, with 1 x 1 convolution to obtain Q
Reverse Decoding Chain. To maximize semantic compensation, we propose the Reverse Decoding Chain. It utilizes reverse chain from deep to shallow layers for decoding compensation, addressing semantic isolation among the current Q, Specifically, F2 has the densest semantic information in
5

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F2, Fal, while in the multi-level text embedding [21 Fsl, the image region perceived by T3 is more global. Therefore, first calculate the Unified Attention of Fa and T3 (not parallel processing [F,, , F2, F3, Fal), and pass through the following formula:
QF < QF + Up(Q,, QF), i 4,3,2
(6)
where UP(æ, y) aims to upsample x to the same scale of y.
After utilizing semantic compensation through the RCTVF module, we generate multi-scale semantic compensated visual features Q' [22] to produce predictions p E RC x W which are compared with the labels or pseudo-labels to
[Q 22 Q3. Qil. Finally, Q" is fed into the segmentation head
calculate the cross-entropy segmentation loss Lee. The overall loss function for the training phase is expressed as follows, where À represents the training weight.
L = Lorth + Lulign + XLice-
Further details about our method and algorithm can be found in Appendix A.2.
Experiments
In this section, we first provide a detailed description of the experimental settings, including the datasets and implementation details, in $4.1. Subsequently, we present the main experimental results of the model in $4.2. Furthermore, $4.3, we conduct comprehensive ablation studies further validate the effectiveness of the CroPe.
4.1 Experimental Settings
Datasets:
To demonstrate the effectiveness of our proposed CroPe method, we conduct experiments across all adverse scenes in seven real-world datasets, including Cityscapes (CS) ACDC[24), Dark Zurich (DZ) Nighttime Driving (ND) BDDI00K-Night (BD) Foggy Zurich (FZ)[28] and Foggy Driving (FD) [29]. Detailed dataset information, including adverse scene types, data splits, and statistics, can be found in Appendix A.3.
Following the prevailing method DAFormer, we adopt CLIP (-B/16 and -1/14 [30]) as the backbone. During training, we use resolution 512x512, rather than the high resolution of 1024x1024 employed by SOTA methods, and omit the FD loss typically used. The initial learning rate for the AdamW optimizer is set to 6e-5, and the learning rates for the encoder, RCTVF module, and segmentation head are 6e-5 scaled by TO x, 10x, 10x, respectively. Additionally, the context length the text prompt is fixed to The attention layers N are set to 6, with two layers computed at each scale. The weight parameter À is set to 2.0. We conduct training experiments for 40,000 iterations. All modules are retained during inference.
Implementation Details:
4.2 Comparison with State-of-the-art Methods
Table 1 presents comprehensive performance comparison between CroPe and existing methods on seven datasets across ten challenging scenarios.
Cityscapes to Foggy Scenes: As shown in Table 1, we compare three foggy scenes, ACDC-Fog, FZ, and FD. Among them, we use CS as the source domain, ACDC-AIl or FZ as the target domain for training, and FD uses the model trained on FZ. for direct generalization testing. CroPe achieves the SOTA performance on all three scenes. On the FD dataset, CroPe improves by 6.2 mloU over DAEN and 4.2 mloU over SAM-EDA. These improvements semantic compensation strategy, which enhances the semantic density of visual features, enabling it to address the challenging conditions such as dense and light fog. It is worth noting that on the ACDC-Fog dataset, CroPe outperforms DAFormer by 10.7 mIoU and DAEN by 5.0 mloU. These demonstrate that CroPe's cross-modal semantic compensation has effective adaptation and scalability abilities to this foggy scene.
probably attributed to CroPe's cross-modal
The nighttime scenes pose the greatest low-visibility challenges. Our CroPe still outperforms nightiie and scene-agnostic models on four nighttime scenes, as shown in Table 1. Specifically, CroPe achieves 62.3 mloU on the CS - DZ, improving by 19.8
Cityscapes to Night Scenes:

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Table 1: Comparison of mloU (%) across four adverse scenarios: Foggy, Night, Rainy, and Snowy. The best accuracy in each column marked in bold, and the second highest is marked underlined. indicates experiments that cannot be implemented using scene-specific models, or the results were not clear for the scene-agnostic models.
Models
Foggy
Night
Rainy
Snowy
All
ACDC-Fog Fog
FZ
FD
ACDh-
DZ
ND
BD
ACDC-Rain
ACDC-Snow
ACDC-AII
Scene-specific Models
CuDA-Net [6]
55.6
49.1
53.5
FIFO [14]
48.4
50.7
FogAdapt [5]
50.6
53.4
SAM-EDA [31]
56.4
GCMA [25]
42.0
45.6
33.2
MCGDA [16]
42,5
49.4
34.9
SWG
51.3
54.2
DAEN [32]
65.6
54.2
54.0
Scene-Agnostic Models
AdaptSeg [33]
26.1
37.6
30.4
34.5
22.0
DAFormer [7]
48.9
40.8
44.7
48.5
51.8
33.9
59.9
53.7
55.4
SaPiCo [19]
58.5
$6.9
40.6
66.1
STA [1]]
60.2
46.9
54,9
48.4
61.3
58.0
60.9
HRDA [8]
69.9
46.0
53.1
55.9
73.6
68.0
MIC
67.0
53.3
56.6
57.2
60.2
58.6
41.3
72.3
70.4
PASS [13]
70.6
$9.9
60,2
60.3
60,2
57.0
43.0
74.6
70.0
70.8
CroPe (Ours)
70.6
60.4
60.6
66.8
62.3
61.6
47.5
74.4
71.4
72.0
Night
Baseline
HRDA
Ground-
Snon
Figure 3: Visualization results comparison with SOTA methods across four adverse scenarios, white dashed boxes and zoomed-in boxes highlighting the different regions.
mIoU compared with MCGDA. In addition, the model trained on DZ shows excellent generalization ability when tested on the ND and BD datasets, improving by 4.6 mloU and 4.5 mloU over PASS, respectively. On ACDC-Night CroPe also achieves the best performance, with 66.8 mloU when trained on the CS + ACDC-AIl dataset and tested on ACDC-Night. These results indicate that CroPe better at overcoming the ambiguity of category boundaries in night scenes.
Comparison on Rainy, Snowy, and All Scenes: As shown Table 1, our CroPe trained on ACDC- All outperforms other methods in rainy, snowy, and all scenarios. On the ACDC-Rain dataset, CroPe achieved 74.4 mloU, which is comparable to PASS. On the ACDC-Snow dataset, the CroPe even surpasses PASS by 1.4 mloU, setting new best benchmark. This performance showcases CroPe's ability to handle challenges such as blurry visual features and occlusions in rainy and snowy scenes while maintaining accurate modeling of both global semantics and small targets. On the ACDC-AIl dataset, CroPe achieved 72.0 mloU, leading the SOTA method PASS by 1.2 mloU. Finally, when the performance of ten datasets is averaged, CroPe surpasses PASS by 2.1 mloU. The above results demonstrate the stable and comprehensive adaptability of CroPe.
Visualization Results: To clearly demonstrate the effectiveness of our method, provide visualization comparison with different methods across night, rain, snow, and fog scenes in Figure 3. As illustrated, the existing methods often produce hallucinations in adverse scenes, such as
7

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Inva Table 2: Ablation studies of proposed key com--
ponents on CS-
RCTVF
Prompt
CPTG
mloU
gain
X
x
x
59.6
+0.0
pan vaaa
X
60.7
+1.1
/
x
61.1
+1.5
/
62.3
+2.7
Figure 4: Convergence curve comparison.
Table 3: Internal ablation study of the CPTG
Table 4:
The ablation study of the Reverse De- coding Chain in RCTVF. The "Mean" column represents the average mloU on the ACDC-All
module on the ACDC-All validation set.
Spec
Comp
mIoU
gain
and DZ validation sets.
X
69.9
Method
ACDC-AII
DZ
Mean
X
X
69.6
-0.3
X
70.6
+0.7
No-Chain
70.7
60.2
65.4
V
x
70.4
+0.5
Forward- Ceward-Chain
69.9
59.8
64.8
V
71.5
+1.6
Reverse-Chie -hhine
72.0
62.3
67.1
misclassifying the sky trees blurring sidewalk boundaries, as emphasized by the rectangular box. In contrast, our CroPe generates more consistent representations by leveraging abstract semantic information from the text modality. Additional visualization results are in Appendices A.7 and A.8.
4.3 Ablation Studies
Effectiveness of Individual Module. Table 2 shows the ablation study of the key components of CroPe. The column "RCTVF" uses the RCTVF module and takes "a typical driving scenario with [class] fixed prompt and image as input, which improves 1.1 mloU compared to pure vision methods. The column "Prompt" substitutes the invariant prompt with learnable prompt, leading to an increase of 1.5 mloU. The column "CPTG" leverages both the complementarity of invariance and domain awareness, achieving 62.3 mloU. These results suggest that the proposed modules can produce synergistic effects, and additional ablation studies are detailed the Appendix A.4.
Furthermore, Figure 4 illustrates the comparison of the convergence curve between Crope and the existing methods. Crope exhibits faster convergence speed, better stability, and higher accuracy.
Analysis of the CPTG Module. This section presents an ablation study of the CPTG module to assess the impact of each prompt, with results detailed in Table 3. The first row illus- trates the performance of directly inputting T into the RCTVF module. The column "Inva" applies Domain-Invariant Regularization to T, resulting in a decrease of 0.3 mloU. This reduction occurs be- cause it overlooks the coupling between the domain- invariant and fomain-speciffe semantic structures in T. leading to blind constraints. The column "Spec" employs Domain SSeecifcc Perception on T', effec- tively aligning the modalities and enabling T to learn domain perception, which improves perfor- mance by 0.5 mloU. The column "Comp" introduces decoupling strategy address the blind constraint issue. Implementing Gated Complementary Fusion after decoupling enhances text embed- ding more effectively. Other ablation experiments of CPTG can be found in Appendix A.5.
Component
Night
Pag
Figure Visualization comparison of F1 and Qi in the ACDC validation set.
Component Analysis the RCTVF Module, This section presents a qualitative and quantitative analysis of the correctness and effectiveness of RCTVE. First, to confirm that multi-scale visual

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Table 5: Ablation results of Lorth and Lalign-
Methods
DZ
ACDC-AIl
ACDC-Night
ACDC-Fog
ACDC-Rain
ACDC-Snow
Crope
48.7
71.5
54.2
78.2
72.1
73.0
w/o Lorth
47.7
68.7
51.3
73.6
68.6
67.9
w/o Latign
48.5
70.1
52.8
74.9
69.9
71.6
Table 6:
Ablation results of CroPe on normal scene understanding tasks. For the training resolution, LR represents 512x 512, while HR represents 1024x 1024.
Methods
Training Resolution
GTA5 to CS
SYNTHIA to CS
DAFormer
LR
68.3
60.9
MIC
LR + HR LR
75.9
67.3
CroPe
74.7
67.6
features can aggregate the semantics of T, we visualize the features Fj and Qi in the first stage of the reverse chain text-visual fusion, as shown in Figure 5. Prior to RCTVF processing, the features of Fi exhibited significant sparsity and window tracking issues. However, after fusing multi-level complementary-pereee perceepiveee text embeddings and performing reverse decoding chain, the visual semantic information is effectively compensated and propagated (as shown in the second row of the figure). This proves that RCTVF achieves cross-modal semantic compensation as described in the method.
Secondly, the ablation experiment of the reverse decoding chain for transferring deep dense semanties is presented in Table "No-Chain" indicates that only the Unified Attention of each layer is computed for semantic compensation, with transmission between different scales. "Forward-Chale to the transmission of semantics from the shallowest to the deepest layer. The performance of Forward- -hhain is worse than No-Chain, with an average decrease of 0.6 mloU, This because the semantic information in the shallow layers lacks the quality and density necessary to enhance the expression of deeper features effectively. Additionally, when using the segmentation head to process visual features across multiple scales, shallow semantic information may fail to integrate effectively with deep features. contrast, Reverse- Reverse-Chhin prioritizes the utilization of high-level features that carry rich semantic information, enhancing the overall semantic fusion effect. Additional ablation experiments related to RCTVF are detailed in Appendix A.6.
refers
Effectiveness of Loss Functions. In our CroPe, two loss functions: Lorth and Latign are proposed. Lorth serves as the core of the decoupling strategy, enabling the MLP effectively decompose the text embedding T into domain-invariant and domain-specific components. Removing this loss may cause the decoupling process fail. Meanwhile, Latign improves the generalizability of learnable prompts during training through generalizable hand-crafted templates (e.g.. "a photo of a [class]"), removing Lalign would eliminate this crucial supervision signal. To verify the effectiveness of these two losses, we further conducted experiments to confirm the essential roles of Lorth and Lalign, as shown in Table 5. The results demonstrate that removing Lorth leads to 2.8 mIoU drop on the ACDC-All dataset, as MLPs fail to decouple, causing redundant and ineffective parameter learning. Likewise, removing Lutign eliminates the generalization constraint provided by hand-crafted prompts (e.g., "a photo of [class]"), resulting in a 1.4 mloU drop. These findings validate the critical role of our design.
Effectiveness under Normal Scenes. Although CroPe is primarily designed for adverse conditions such fog nighttime, it can also be applied to normal scene understanding. To verify this, we conducted experiments on two standard domain adaptation tasks (GTAS - Cityscapes and SYNTHIA + Cityscapes). these experiments, we adopted general prompt "a photo of [class]" instead of "a typical driving scenario with [class]." As shown in Table 6, the results show that CroPe achieves improvements 6.4 mloU and 6.7 mloU over the baseline (DAFormer), respectively. These findings are particularly encouraging, with smaller domain gaps and less severe visual degradation, where the impact of our modules may be weakened. While CroPe demonstrates strong performance in adverse scenarios, these new
they highlight the robust performance of CroPe even in scenarios

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Table Analysis of model complexity, training cost, and inference speed.
Method
Params
Time GPU Usage
Inference Speed
mloU
HRDA(SegFFrmer))
85.69 M
17 h
23.5 GB
2.00 img/s
68.0
MIC(SegFormer)
85.69 M
23
23.5 GB
1.82 img/s
70.4
PASS(SegFormer)
85.69 M
25 h
23.5 GB
1.82 img/s
70.8
CroPo(ViT-B/16 w Frozen)
27.36 M
6h
5.7 GB
5.12 img/s
67.0
CroP:(ViT--B16 w Full)
114.19 M
9h
11.0
5.10 img/s
68.6
CroPe(ViT-B/16 LoRA)
43,91 M
8h
7.2
4.98 img/s
68.3
CroPe(Vit-L/14 Frozen)
36.34 M
9h
8.9 GB
2.67 img/s
69.7
CroPe(ViT-L/14 w Full)
341.37 M
12 h
18.0 GB
2.64 img/s
72.0
CroPe(ViT-L/14 LoRA)
64.70 M
10h
12.0 GB
2.60 img/s
71.4
Table 8: Comparison of the parameters in each module of the method.
Method
Total
Visual Encoder
CPTG
RCTVF
MIC(SegFormer)
85.69 M
81,44 M (95.05%)
CroPe(ViT-L/14 w Full)
341.37 M
214.64 M (69.35 (6933%)
10.24 M (2.99%)
9.39 M (2.75%)
CroPe(ViT-L/14 w LoRA)
64.70
37.78 M (58.38%)
10.24 (15.83%)
9.39 M (14.51%)
experiments further validate the generalization ability and scalability of the proposed prompt-based adaptation framework in normal scenes.
Complexity Comparison and Optimization. Although the CroPe shows significant performance advantages, the increase in training parameters caused by its multi-modal design still raises concerns about deployment feasibility. This section quantitatively analyzes the efficiency of the CroPe through systematic experiments and introduces lightweight adaptation strategy to optimize scalability. Table 7 shows the trainable parameters (Params), training time (Time). GPU memory usage (GPU Usage), inference speed, and mloU performance comparison on the ACDC dataset. The experiment covers four types of models: I) traditional SegFormer [34] variants: 2) CroPe variants with frozen backbones; 3) CroPe with full fine-tuning: 4) CroPe with LoRA [35] parameter-efficient fine-tuning.
We conduct all experiments on a single RTX4090. can be seen that methods based on the SegFormer (lines 1-3) generally occupy nearly 24GB of memory, have an average training time of more than 20h, and an inference speed of less than 2 img/s. This is mainly due to its use of large-resolution training and multi-branch forward strategy to ensure performance. All forms of CroPe can better achieve the balance between efficiency and performance. For example, CroPe achieves certain performance with less than 10 GB of memory and less than 10 of training time when the backbone network is frozen (lines 4 and 7). The fully fine-tuned CroPe further maximizes the performance (lines and 8) without significantly increasing the training cost. However, the increase its parameter raises key question: Is CroPe still competitive under the same parameter adjustment? Therefore, we introduce the LoRA strategy apply low-rank projections with rank r = 64, scaling factor 2r, and dropout=0.1 to the q and branches in VIT (lines and 9), achieving better performance-effiieer limited limits. The specific parameter analysis of each module within CroPe are shown Table
balance under
Conclusion
In this paper, we present CroPe, a Cross-Modal Semantic Compensation Adaptation method for UDA scene understanding in adverse scenarios. We introduce the pepplemenntar-Pereppttiii Text Generation module to enhance the cross-domain semantic representation of text and develop the Reverse Chain Text- Visual Fusion module to improve the consistency of multi-scale visual features by incorporating dense semantic embeddings of text. Extensive experiments demonstrate that CroPe enhances domain-invariant feature learning, alleviating model hallucinations and instability in various adverse scenes. However, CroPe certain limitations, including an increase in model parameters. In future work, will address these challenges and further optimize the model.
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Acknowledgements
We thank the anonymous referees for their constructive comments which have helped improve the paper. This work was supported by the National Natural Science Foundation of China (Grant Nos.: 62576006 and 7247 72471001), the Natural Science Foundation for the Higher Education Institutions of Anhui Province (Grant No.: KJ202IA0038), the Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence at Zhejiang University (Grant No.: BM12400004), the Natural Science Foundation of Anhui Province (Grant No.: 24080853037).
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A Technical Appendices
The technical appendix and supplementary materials are organized as follows: 1) Section A. gives more related works to help quickly understand the state state-of-the-ant work in this field; 2) Section A.2 contains the architectural details and algorithmic supplements of CroPe; 3)Section A.3 contains detailed dataset information, including adverse scene types, data splits, and statisties. 4) Section A.4 provides additional complete component ablation studies; 5) Section A.5 performs an additional prompt ablation study in the CPTG module; 6) Section A.6 presents a quantitative analysis and additional experiments that examine the performance of the RCTVF module; 7) Section A.7 contains visualization of feature maps after processing by the RCTVF module; 8) Section A.8 contains more experiments on CroPe segmentation visualization: 9) Section A.9 contains potential societal impacts.
A.1 More Related Work
In order to reduce the difference in feature distribution between domains, UDA proposes a variety of methods to bridge the gap between the source domain and the target domain. Existing research can be mainly divided into two paradigms: adversarial training and self- self-training. Adversarial training [36, 37, 38] aims coordinate the outputs across domains by aligning the distributions of different domains at different levels such as input, features, and output. However, this method is often affected by instability [39], which limits its cross-domain effect. In contrast, self-training [40, 41] jointly optimizes the distribution of the source domain and the target domain through pseudo-label learning and gradually improves the performance of the target domain. In recent years, HRDA [8], which relies on self-training achieves a balance between preservation of high-resolution detail and perception of long-range context through multi-resolltion training (large low-resolution context + small high- resolution details) and scale attention mechanism. The core challenge of this paradigm is how to extract reliable pseudo-labels. Therefore, many studies conduct extensive explorations from the perspectives of confidence threshold setting [42] and pseudo-label correction [43, 44]. Our work also belongs to the self-training paradigm and further proposes a better solution on this basis.
A.2 Network Architecture Details
We propose a cross-modal semantic compensation method to improve the consistency of the model in adverse scenarios. CPTG enhances the domain awareness and generalization ability of text, while RCTVF compensates for visual semantics with improved textual clues.
CPTG decouples text embedding into two components (invariant and specific) through decoupling strategy. It aligns the specific component with CLIP visual features via Jomain-Speciffi Perception and regulates the generalization of the invariant component through Domain- Invariant Regularization. Gated Complementary Fusion then fully integrates the two decoupled features, providing rich text priors for RCTVF. In RCTVE, multi-scale visual features interact with text cues through Unified Attention, where visual features serve as queries and the embeddings generated by CPTG function as key-value pairs. This process effectively fuses multi-level textual semantics into the visual modality while providing semantic compensation. Additionally, we connect multi-scale features through a Reverse Decoding Chain enhance the fusion effect and propagate refined semanties from fine- grained to coarse-grained layers. The complementary design of the two modules ensures that CPTG focuses on cross-domain guidance, while RCTVF tackles the issue of visual semantic sparsity. By applying segmentation loss to the multi-scale output of RCTVF, we achieve joint optimization of text and visual modalities, enabling simultaneous training for visual-text alignment and UDA adaptation performance. The complete training process of our CroPe is illustrated in Algorithm 1.
A.3 Detailed Datasets Information
Cityscapes (CS) is captured under normal weather conditions in 50 European cities, containing 2,975 training images, 500 validation images, and 1,525 test images. ACDC contains four adverse scenes:
fog, rain, snow, and night. For each scene, there are 400 training images, 100 validation images (including 106 nighttime images), and 500 test images. along with 1,600 clean reference images (ACDC-ref). Dark Zurich (DZ)) provides 8,779 images captured during nighttime, twilight, and daytime, with 50 validation and 151 test images. Nighttime Driving (ND) includes 50 coarsely annotated nighttime images specifically designed for testing. BDDI00K-Niee -iight (BD), subset of the
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Algorithm The core algorithm in CroPe
Input: Input image I. hand-crafted text prompt my and learnable text prompt 72,, visual encoder By and text encoder Ex.
Output: Multi-Scale Semantic Compensated Visual Features Q"
# Obtain encoder features for visual and textual modalities
2: Feed / into Ey: P = {Pi. P2, P3. P1 = (v(
3: Multi-scale visual features: F FPN = (Fi, F2, Fs, Fa).
4: Feed m1, my into Ey: Tc = Er (m1)))). T Ey (m2).
5: # Seaplementtay-Perrepphee
Text Generation (CPTG) module
6: Decouple the embedding 7 into I'y and Ts. and pass the orthogonal constraint function. (Eq. 1)
7: The soft consistency function constrains Ty to stay close to Te, maintaining the generality of Ty.
[Eq. 3)
8: Ts combined with (P2, P3, P1} respectively to obtain the 7 = (TT, T3, (Eq. 2)
9: T' is fused with 7 to obtain multi-level conplemeentrry-peroppptss text embeddings T
[7,, T2: 7a). (Eq. 4)
10: Reverse Chain Text- Visual Fusion (RCTVF) module
11: The projection QF, of F; and T;-1 are processed by Unified Attention to get Q7, which is then
upsampled and fused with QF, i 4, 3, through Reverse Decoding Chain. (Eq. 5, Eq. 6)
12: The semantically compensated Q' is passed through the segmentation head to obtain logits p.
Table 9: The number of training, validation, and test sets for each dataset. "_" represents missing.
Images
CS
ACDC-AIl
ACDC-Fug
ACDC-Snow ACDC-Night
ACDC-Rain
DZ
ND
BD
FZ
FD
Traing set
2975
1600
400
400
400
400
8779
3808
Validation
500
406
100
106
100
100
50
Test set
1525
2000
500
500
500
500
151
50
87
40
101
BDD100K segmentation dataset, consists of 87 finely annotated nighttime images. Foggy Zurich (FZ) contains 3,808 images with light and medium fog, and 40 images for testing. Foggy Driving (FD) provides 101 annotated images purely for testing. For more structured statistics, see Table 9.
A.4 Component Ablation Study
In the main text, we conducted an ablation study on the effectiveness of each module, focusing on the impact of incorporating the CPTG and RCTVF modules into our proposed CroPe model. Building on this, we provide a more comprehensive set of ablation experiments following the naming convention established in Table 2, including replacing the backbone network and choosing different CLIP variants to illustrate the evolution from the bascline DAFormer to proposed CroPe.
The results are summarized in Table 10. where we added two columns for clarity: the column "V-only", which is checked if the visual backbone network of CLIP is used (ViT-B and ViT-L are two considered variants); and the column "w/o FD", which checked if the FD loss strategy of DAFormer is discarded; otherwise, it is retained. We initially conducted experiments by replacing the backbone network alone (replacing SegFormer with CLIP's ViT-B or ViT-L). This modification leads to marginal performance gains, with mloU improvements of 3.4 and 6.6, respectively. While replacing the backbone improves overall performance, it also exposes a key limitation: the FD loss in DAFormer (originally designed to align encoder features with those of frozen encoder pre-trained on ImageNet) turns out to be suboptimal. This misalignment occurs because the CLIP encoder is not pre-trained on ImageNet, which can affect convergence efficiency and discriminability. To address this, we systematically remove the FD loss (indicated in the w/o FD column), resulting in further improvements of 3.4 and 4.5 mloU for VIT-B and ViT-L, respectively. This validates the redundancy of the FD strategy when using a cross- modal backbone.
We then integrate handcrafted text prompts "a typical driving scenarios with [class] into the RCTVF module (column "RCTVF"), which improves the performance of ViT-B and ViT-L by 2.2 mloU and 1.1 mloU, respectively. This demonstrates that combining RCTVF with invariant information can enhance the semantic density of visual features through unified attention fusion and reverse decoding chain. Replacing the fixed text prompts with learnable prompts (column "Prompt") can capture
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Table 10: Albation studies of proposed modules on CS-DZ using SegFormer, ViT-B/16 and -L/14
Method
Backbone
V-only
FD
RCTVE
Prompt
CPTG
mloU
gain
DAFormer
SegFonner
x
X
X
X
48.5
X
K
51.9
+3.4
X
55.3
+6.8
CroPe
VIT-B/16
57.5
+9.0
X
57.9
+9.4
59,4
+10,9
X
X
K
55.1
+6.6
X
59.6
+11.1
CroPe
VIT-L/14
60.7
+12.2
61.1
+12.6
62.3
+13.8
Table 11: Ablation experiments using different hand-crafted text prompts, the best results each dataset are shown in bold.
Source
ACDC Night ACDC Snow
ACDC Rain
ACDC Foggy
"a photo of a [class]
60.7
68.8
72.0
67.3
"a clean origami of [class]
61.2
68.7
72.2
67.4
CS
"n image of driving with a [class]
59.7
67.4
72.0
67.0
"a typical driving scenario with [class]
61.5
69.3
72.5
68.1
domain-specific features more flexibly, leading an additional improvement of 0.4 mloU. Finally, incorporating a complementary perceptual mechanism (column "CPTG") preserves both domain invariance and domain awareness, ultimately achieving mloU scores of 59.4 and 62.3 for ViT-B and ViT-L, respectively, These findings highlight the efficacy and synergistic benefits of the proposed components, providing strong rationale for the design of the CroPe model.
A.5 More CPTG Ablation Studies
In the main text, we conducted ablation experiments inside the CPTG module to explore the effects of using certain type of text prompt alone and in combination with different prompts. In this section, we further conduct an ablation study on the CPTG module to further evaluate the processing effects of various prompts in CPTG and give visual analysis of CPTG.
First, we conducted selection experiments on the aand-rrafted text prompts listed in Table 11. Considering that the knowledge based on ViT-B is more unstable and more sensitive to hand-crafted text prompts, we chose this model to verify the effects of various prompts four different scenarios of the ACDC dataset. The experimental results show that the prompt "a typical driving scenarios with a [class]" shows significant advantages in all indicators. This is mainly due to the fact that the prompt provides more general semantic representation of the driving scene, which is closer to the actual application scenario than "a photo of [class]". ". However, the latter can still achieve relatively ideal results, and in future domain adaptation tasks involving non-driving scenarios, more general prompts obviously have greater application potential.
Secondly, through the qualitative ablation analysis of the CPTG in Figure 6, we can intuitively understand the necessity of the CPTG. The results show that in the absence of the CPTG, cross-layer fusion relying solely on visual features cannot effectively alleviate the serious visual information occlusion problem. By introducing semantic compensation of text prompts, the sparsity of features can be significantly improved, thereby effectively alleviating the serious domain shift problem.
A.6 More RCTVF Ablation Studies
In the main text, we illustrated the impact the RCTVF module's fusion strategies and the feature distribution before and after RCTVF processing through various figures and tables. To further
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A
Input
(b) Ground Truth
(c) w/o TCP
(d) w/ TCP
Figure 6: Use CroPe model to perform visual comparison of the ACDC-AIl validation set samples with and without the CPTG module.
Table 12: RCTVF ablation on the ACDC-All validation set, where denotes the visual modality only and "OOM" indicates oft-or-memory.
Batch Size
MIC
Ours'
Ours
66.5
67.9
69.8
2
69.6
00M
71.5
quantify the domain adaptation performance gains brought by the RCTVF module, present the Maximum Mean Discrepancy (MMD) metric between the two domains for each category in the Cityscapes ACDC-AIl task, shown Figure 7. The results demonstrate that CroPe achieves significantly lower MMD distances compared to MIC across all categories. Notably, in the more challenging categories such train, rider, and motorcycle, CroPe further reduces the MMD by 0.0796, 0. 169, and 0.0500, respectively. This reduction highlights RCTVF's capability to extract more robust feature representations, effectively narrowing the domain gap and enabling the model to better learn domain-invarinn features.
Furthermore, while the RCTVF module in the main text presented solely within the context of vision-text multimodal approach, it is also designed to handle pure visual modality inputs, resembling self-attention mechanism. To explore this versatility, compare the performance of RCTVF under pure visual modality and cross-modal strategies Table 12. The results reveal that, although the RCTVF's
pure visual modality (third column) outperforms MIC (second column)-unders adaptability and effectiveness it remains constrained by inherent visual interference, which limits its ability capture dense semantic information effectively. The introduction of the textual modality (fourth column) mitigates this limitation, further improving performance and reinforcing the necessity of cross modal semantic compensation for effective domaín adaptation. These findings collectively affirm the RCTVF module's critical role in enhancing robustness and semantic richness in UDA.
A.7 Feature Visualization Experiment
To verify the effectiveness of semantic compensation of visual features across multiple scales as pointed out by the RCTVF module, we visualized the feature maps of Q" (Qi, Qú, Q3, Qi) on the validation set of each scene of ACDC. As shown in Figure 8. the proposed CroPe demonstrates hicrarchical semantic refinement and cross-scale consistency enhancement. Specifically, the deepest scale Qi absorbs dense textual semantics from T3 and resolves the ambiguity caused by domain shift; while the reverse decoding gradually propagates high-level semantics shallow scales, thereby sharpening the details of small objects (c.g., road poles in night scenes). This experiment further

---

Methods
ZIG
Ours
MMD
0.3
6. 0,000
0.2
0.076
0015
0.0239
0.1
0.8705
0.046
6.0168
0.077
8.0059
road
sidewalk
building
wall
fence
traffic light
pale
traffic sign
vegetation
terrain
sky
person
rideer
truck
bus
motorcycle
train
bicycle
Classes
Figure 7: Comparison of Maximum Mean Discrepancy (MMD) distance, where the values represent the differences for each category.
Input
Stage!
Stage2
Stage3
Stage4
Flog
Night
Figure 8: Visualization of the feature maps of the semantically compensated visual features of each stage (scale) of the CroPe model on the ACDCCFig/NighhRRainSSnn validation set.
confirms that semantic compensation enhances feature density and domain-invarian representation learning, resulting in clearer semantic boundaries and fewer misclassifficaions in adverse scenes.
A.8 More Segmentation Visualization Comparison
In Figures 9-12, we show qualitative segmentation results compared with Baseline (DAFormer) and MIC on the validation set of four different scenes: Cityscapes - ACDC-(Fog, Rain, Night, Snow). Compared with MIC, the masks predicted by our model have finer details near the object boundaries, thanks CroPe's cross-modal semantic compensation, which makes up for the shortcomings of visual semantic sparsity. Compared with the Baseline, we have significantly reduced the hallucination phenomenon of the model. It should be noted that CroPe does not actually use DA Forrerr's FDloss and Segformer backbone, which we have explained in the text.
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Input
Baseline
MIC
Crope(Ours)
Ground Truth
Figure 9: Qualitative visual comparison of the proposed CroPe with existing state-of-the-ard methods on the ACDC-Fog validation set
Input
Baseline
MIC
Crope(Ours)
Ground Truth
Figure 10: Qualitative visual comparison of the proposed CroPe with existing oftte-of-the-art methods on the ACDC-Rain validation set
Input
MIC
Crope(Ours)
Truth
Figure 11: Qualitative visual comparison of the proposed CroPe with existing state-of- state-of-the-al of-the-art methods on the ACDC-Night validation set
Input
Baseline
MIC
Crope(Ours)
Ground Truth
Figure 12: Qualitative visual comparison of the proposed CroPe with existing state-of-the-art methods on the ACDC-Snow validation set
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A.9 Potential Negative Societal Impacts
Our method poses no ethical risks regarding dataset usage or privacy violations, as all datasets and tools are publicly available and transparent.
27


Reviews:
[{"review_id": "fd9kBRgTDD", "reviewer": "Reviewer_xJhp", "summary": "This paper proposes a cross-modal semantic compensation adaptation method for robust scene understanding in adverse conditions (fog, snow, and night). Existing approaches only rely solely on visual information, CroPe introduces textual information to provide consistent representations. CroPe includes CPTG to provide text embeddings, RCTVF to conduct cross-modal compensation. Experiments show the effectiveness.", "questions": "1. It is not clear why the textual information can provide 'natural generalization ability' (line37-38) for adverse scenarios as texts only provide class information without fog, snow, or night information.\n2. How to provide pseudo label information? Does it keep the same as baselines?\n3. The paper lacks the ablation about L_{orth} and L_{align} to prove whether the decouple and domain-invariant regularization is essential.", "limitations": "See questions.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. CroPe introduces textual information to enhance performance in adverse conditions and experiments show the effectiveness.\n2. CroPe provides sufficient experiments to prove the effectiveness.\n\nWeakness: \n1. Introducing textual information is common for several segmentation tasks, the authors borrow this to adverse conditions, but the motivation and reason seems not clear and strong enough. See questions.", "was_revised": true, "final_justification": "I have carefully considered the paper and the authors' rebuttal. The authors have addressed my concerns. After thorough evaluation, I maintain my recommendation of Borderline Accept (4).", "rebuttal": "Many thanks for reviewing our work and providing these valuable feedback.\n \n**W1:** First, although introducing textual information is common in segmentation tasks (e.g., referring segmentation, open-vocabulary segmentation), our motivation for introducing text in the context of unsupervised domain adaptation under adverse conditions (UDASS) is totally different from them. As described in Introduction section, our CroPe introduces text modality to offer semantic compensation for visual features in adverse scenes (e.g., fog, snow, nighttime). In UDASS, visual features often suffer from severe degradation, including loss of texture, low visibility, and color distortion. In such cases, relying solely on visual cues becomes insufficient. We propose that textual semantics, which remain unaffected by environmental changes, can serve as stable high-level guidance to compensate for missing or ambiguous visual details under adverse conditions. Second, our innovative design of complementary fusion prompts and semantic compensation underscores its uniqueness, with our motivation robustly validated by experimental results. On ACDC-Night, where visual degradation is most severe, our method achieves +6.5 mIoU over existing works. \nWe will further emphasize these motivations in the revised version to enhance clarity. Thanks again for your insightful feedback.\n \n**Q1:** The goal of the task is to precisely provide the semantic labels for the target pixels in different conditions, e.g. fog, snow, nighttime. Visual features are sensitive to changes in illumination, texture, and occlusion patterns, while text providing class information ensures semantic stability across conditions and domains. In our methods, we design to provide a textual prompt for each class in an image and get new SOTA  performance. Therefore, we mention that the textual prompt can provide 'natural generalization ability', as the textual information captures the semantics for the same class in different domains which is invariant to visual conditions. The stable textual features offer semantic compensation for visual features in cross-domain settings, thereby improving the generalization ability of cross-domain sementic segmentation.\n \n**Q2:** The pseudo-labels of our method are generated following a standard self-training strategy, which is consistent with the baselines.Specifically, we use a teacher model to produce prediction probabilities, which are then converted into one-hot labels as pseudo-labels. This process is identical to the baselines, ensuring the fairness. We will clarify this in the Experiment section of revised manuscript.\n \n**Q3:** $L_{orth}$ is pivotal to the decoupling strategy, enabling MLPs to effectively decompose text embeddings $T$ into domain-invariant and domain-specific components; without it, decoupling fails, making separate ablation unnecessary. $L_{align}$ enhances the generalization of learnable prompts during training through generalizable handcrafted prompts (e.g., \"a photo of a [class]\"); omitting $L_{align}$ removes this critical supervision signal. To further address your concerns , we conduct supplementary experiments confirming the essential roles of $L_{orth}$ and $L_{align}$ in the CPTG module.\n\n| Methods | CS to DZ | CS to ACDC-All | CS to ACDC-Night | CS to ACDC-Fog | CS to ACDC-Rain | CS to ACDC-Snow |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| CroPe |  48.7|  71.5| 54.2 | 78.2 | 72.1 | 73.0 |\n| w/o $L_{orth}$ | 47.7 | 68.7 | 51.3 | 73.6 | 68.6 | 67.9 |\n| w/o $L_{align}$ | 48.5 | 70.1 | 52.8 | 74.9 | 69.9 | 71.6 |\n \nThe results demonstrate that removing $L_{orth}$ leads to a 2.8 mIoU drop on the ACDC-All dataset, as MLPs fail to decouple, causing redundant and ineffective parameter learning. Likewise, removing $L_{align}$ eliminates the generalization constraint provided by handcrafted prompts (e.g., \"a photo of a [class]\"), resulting in a 1.4 mIoU drop. These findings validate the critical role of our design. We will integrate these analysis into the revised manuscript.\n \n**Paper Formatting Concerns:** Thank you for pointing out the formatting issue. Upon careful review, we found that the image resolution was reduced during the conversion process, resulting in unclear visual quality. We have re-exported high-resolution versions of the affected figures and will update them in the revised manuscript to ensure clarity."}, {"review_id": "rQ48BzxQD9", "reviewer": "Reviewer_U3Dv", "summary": "This paper proposes a cross-modal semantic compensation adaptation method, CroPe, which generates multi-level text embeddings integrating generalization and domain awareness through a Complementary Perceptive Text Generation (CPTG) module. It also utilizes a Reverse Chain Text-Visual Fusion (RCTVF) module to sequentially fuse compensation information from deep to shallow features, maximizing compensation gain to address the challenge of visual appearance degradation in scene understanding.", "questions": "See Weaknesses.", "limitations": "yes.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### **Strengths**:\n1. CroPe introduces the text modality, leveraging its generalization ability to provide semantic compensation for visual features, enabling the model to learn cross-domain consistent representations and reduce model hallucination.\n2. Through the CPTG and RCTVF modules, it generates multi-level complementary perceptive text embeddings to maximize the compensation gain for visual features.\n3. While ensuring performance, CroPe demonstrates higher efficiency, achieving a good balance between efficiency and performance.\n\n### **Weaknesses**:\n1. The authors need to highlight the core contribution of CroPe. Is there a central innovation? The paper reads more like an engineering technical report. Although the process and details of the method are described clearly, it does not clarify why each module is designed as such or what the advantages of each module are.\n2. The trainable parameters of the model in Figure 2 are ambiguous. Do components like the Transformer decoder, FPN, and RCTVF include trainable parameters?\n3. Why does the Domain-Specific Perception module use a Transformer decoder instead of an encoder for cross-modal fusion?\n4. In extremely harsh scenarios (e.g., dense fog), where visual features may severely degrade, will text semantics overly dominate, causing segmentation results to deviate from the actual scene?\n5. Domain-invariant regularization relies on manually designed text prompts (e.g., \"a typical driving scenario with a [class]\"), whose generalizability is limited by scene diversity.", "was_revised": true, "final_justification": "This framework is an engineering work that focuses on practical problems, but it lacks novelty and innovation. I tend to maintain the original score.", "rebuttal": "Thanks for reviewing our work and providing these valuable comments.\n \n**W1:** Below is our clarification for all questions.\n\n1) **Core contribution and innovation:** Our CroPe pioneers the integration of robust text modality semantics into unsupervised domain adaptive semantic segmentation (UDASS) under adverse conditions, addressing the shortcomings of existing visual-only methods in visual feature degradation (e.g., night, fog). As described in introduction section, in UDASS, visual features often suffer from severe degradation, including loss of texture, low visibility, and color distortion. In such cases, relying solely on visual prompt engineering (e.g., mask reconstruction of MIC and image style augmentation of PASS) becomes insufficient. We propose that textual semantics, which remain unaffected by environmental changes, can serve as stable high-level guidance to compensate for missing or ambiguous visual details under adverse conditions. Thus, we propose a cross-modal semantic compensation approach to enhance the generalization of visual features across diverse adverse scenarios. We validated the advantages of our modules through comprehensive experiments. For example, CroPe achieves a substantial 6.5 mIoU improvement on the ACDC-Night dataset (Table 1). Moreover, it reduces training time by 52–64%, memory usage by 28–64%, and boosts inference speed by ~45%, while enhancing scalability (Table 5).\nIn summary, our method surpasses traditional visual frameworks, effectively addressing visual degradation in UDASS (e.g., preventing sky missegmentation as roads) from both theoretical and practical perspectives.\n  \n2) **Why each module is designed as such:** Our CroPe consists of two core modules, RCTVF and CPTG, for unsupervised domain adaptive semantic segmentation (UDASS) in adverse conditions. As described in Introduction section (line 36-53), the RCTVF module is designed based on the ability of text information to capture stable semantics for the same category across different domains (derived from pre-trained knowledge of CLIP), which remain invariant to visual conditions. Thus, fully integrating text semantics into multi-scale visual features effectively compensates for semantic degradation in visual features. The RCTVF module introduces a unified attention mechanism, embedding text semantics of CLIP into multi-scale visual features and refining fused features layer-by-layer via a reverse decoding chain, achieving robust cross-modal semantic compensation. The CPTG module is designed to address the risk of text prompts overfitting to the source domain and misaligning with the visual modality (line 97-103). Therefore, we use orthogonal loss to decouple domain-specific and domain-invariant semantics, with gated complementary fusion dynamically balancing both domain perception and generalization.\n\n3) **What the advantages of each module are:** For the CPTG module, Table 2 shows that its introduction improves 1.2 mIoU, confirming that enhancing text embeddings with domain-specific perception and generalization constraints boosts model adaptability. Table 3 provides an ablation study on internal components of CPTG, further validating the effectiveness of its domain-specific perception module and generalization constraints. Table 9 demonstrates the impact of different fixed prompts on generalization, indicating the ability to adapt to various general prompts. For the RCTVF module, Table 4 verifies the superiority of the reverse decoding chain in fusion performance. Table 8 directly compares model performance with and without RCTVF, showing an average mIoU improvement of 1.6, proving the effectiveness of cross-modal fusion. Table 10 further shows that, even with only visual modality input, RCTVF outperforms existing methods (e.g., MIC), highlighting its unique value in enhancing visual feature robustness.\n \n**W2:** In Figure 2, the trainable parameters are located in the Transformer decoder, FPN, and RCTVF components. Only the CLIP text encoder, which is explicitly marked with a “frozen” icon, is not trainable.\n \n**W3:** We use a Transformer decoder in the domain-specific perception module for the following reasons:\n\n1) Compared to cross-attention mechanism in encder, the decoder more effectively preserve the complete semantics of text prompts while dynamically integrating visual features via textual embeddings. In UDASS adverse scenarios (e.g., night, fog), the decoder leverages prompt knowledge (e.g., class semantics) to robustly compensate for visual degradation, while the encoder's cross-attention risks disrupting prompt semantic consistency.\n2) To ensure a fair comparison and align with established practices in prior work (e.g., [1-5]), we adopt a Transformer decoder in the Domain-Specific Perception module for effective cross-modal fusion.\n \nFor further validation, we compare the decoder and encoder on the CS to DZ and CS to ACDC datasets. The decoder architecture outperforms the encoder by an average of 2.1 mIoU.\n| Methods | CS to DZ | CS to ACDC-All | CS to ACDC-Night | CS to ACDC-Fog | CS to ACDC-Rain | CS to ACDC-Snow |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| Decoder |  48.7|  71.5| 54.2 | 78.2 | 72.1 | 73.0 |\n| Encoder |  47.0|  69.5|  50.5 | 76.5 | 70.9 | 70.5 |\n \n[1]Image segmentation using text and image prompts, CVPR 2022.\n[2]Segment anything, ICCV 2023.\n[3]Per-pixel classification is not all you need for semantic segmentation, NeurIPS 2021.\n[4]Textual query-driven mask transformer for domain generalized segmentation, ECCV 2024.\n[5]Masked-attention mask transformer for universal image segmentation, CVPR 2022.\n \n**W4:** In extremely harsh scenarios (e.g., dense fog), visual features may be severely degraded, and text embeddings may contribute more during fusion. However, CroPe ensures segmentation results do not significantly deviate from the actual scene through the Domain-Specific Perception module before text-visual fusion. This module aligns text embeddings with scene-specific information (e.g., texture, lighting) via an attention mechanism, enabling text embeddings to perceive the current visual context and reduce modality disparity. Table 3 shows the Domain-Specific Perception module improves mIoU by 0.7 on ACDC-All, validating its efficacy in extreme scenarios. We will supplement related explanations in the revised manuscript.\n\n**W5:** First, handcrafted text prompts (e.g.,\"a typical driving scenario with a [class]\") are designed to constrain learnable prompts, which are prone to overfitting the source domain (labeled) during training, enhancing generalization in cross-modal semantic compensation and preventing insufficient visual semantic enhancement. The CPTG module achieves this through domain-invariant regularization $L_{align}$. \n\nSecond, regarding generalizability: In Table 9 of our paper, we compare multiple handcrafted prompts, including generic \"a photo of a [class]\" and the more scene-specific \"a typical driving scenario with a [class]\". The two prompts differ by only a marginal 0.8 mIoU compared to our overall improvement of 6.5 mIoU in the ACDC-Night dataset, indicating that the generic prompt is also sufficient for achieving strong performance, **without affecting generalization**. We use the scene-specific prompt \"a typical driving scenario with a [class]\" because current UDASS benchmarks, especially those under adverse conditions, are almost exclusively focused on driving scenes (e.g., TPAMI 2023: Domain adaptive and generalizable network architectures and training strategies for semantic image segmentation). Hence, prompts like \"a typical driving scenario...\" are designed specifically to enhance performance under these conditions. For broader applicability, prompts such as \"a photo of a [class]\" offer more general semantic coverage and can be used for non-driving domains."}, {"review_id": "tmv5xWt9uQ", "reviewer": "Reviewer_jRMA", "summary": "The paper introduces CroPe, a novel Cross-Modal Semantic Compensation Adaptation framework designed for unsupervised domain adaptation (UDA) in adverse scene understanding tasks (e.g., fog, snow, rain, night). Unlike previous visual-only approaches, CroPe incorporates textual semantics via a vision-language framework to compensate for degraded visual features under challenging conditions.", "questions": "How robust is the model to poor or noisy prompts (e.g., misphrased or low-quality language inputs)?\n\nDoes the proposed method also work well in normal scene understanding?", "limitations": "The full fine-tuning version increases parameters significantly, posing challenges for deployment on resource-constrained systems.\n\nThe proposed method still depends on manually designed prompts; automated or adaptive prompt generation remains unexplored.\n\nThey assume paired vision-text representation (e.g., CLIP) and may struggle in environments with weak textual priors or unseen categories.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Weaknesses:\n1. Domain-specific and domain-invariant representation learning is a traditional and popular approach in addressing domain shift problems. Though the author investigates the literature of UDA, they should better clarify the difference between the proposed method and existing methods, such as methods in [1].\n\n[1] Wang J, Lan C, Liu C, et al. Generalizing to unseen domains: A survey on domain generalization[J]. IEEE transactions on knowledge and data engineering, 2022, 35(8): 8052-8072.\n[2] Zhou K, Liu Z, Qiao Y, et al. Domain generalization: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 45(4): 4396-4415.\n\n\n2. Incorporating text may introduce architectural and training complexity compared to vision-only baselines, and heavily rely on manual prompt.\n\n3. CroPe(ViT-L/14 w Full) has 341.37M, which largely impedes the application in real-world scenarios. Please clarify this.\n4. Performance relies on handcrafted textual prompts (e.g., \"a typical driving scenario with a [class]\"), requiring careful design per task/domain.", "was_revised": true, "final_justification": "Considering some minor concerns about efficiency and visualizations remain unaddressed, I tend to maintain my score at this stage.", "rebuttal": "Many thanks for reviewing our work and providing these valuable suggestions and feedback.\n \n**W1:** Thanks for reviewer's suggestion. Existing domain-specific and domain-invariant representation learning methods in [1] and [2], such as domain adversarial learning [3], explicit feature alignment [4], invariant risk minimization [5], and generative modeling [6], primarily rely on the visual modality to align or disentangle features across domains. In contrast, our method innovatively decouples the internal embeddings of learnable text prompts, with domain-invariant embeddings capturing generalization and domain-specific embeddings enabling modality alignment by perceiving visual domain information. Furthermore, we introduce a gated complementary fusion module to dynamically balance the domain-perception and generalization capabilities of prompts. This fusion mechanism emphasizes a unique contribution, distinctly setting our work apart from prior methods. We will include a detailed comparison with related methods in the revised manuscript to highlight these differences.\n\n[1]Generalizing to unseen domains: A survey on domain generalization, TKDE 2022. \n[2]Domain generalization: A survey, TPAMI 2022.\n[3]Unsupervised Domain Adaptation by Backpropagation, ICML 2015.\n[4]Domain Adaptation via Transfer Component Analysis, TNNLS 2010.\n[5]Representation Learning via Invariant Causal Mechanisms, arXiv.\n[6]DIVA: Domain Invariant Variational Autoencoders, MIDL 2020.\n \n**W2:** Below is our clarification for all questions.\n\n1) **Regarding architectural and training complexity:** CroPe does not significantly increase computational overhead. On the contrary, it achieves superior efficiency compared to vision-only methods through efficient module design and low-resolution training. Specifically, CroPe is trained at a 512×512 resolution, substantially reducing computational costs compared to traditional vision-based methods (e.g., MIC, PASS), which rely on high-resolution training and complex strategies (e.g., mask reconstruction or feature distance regularization). As shown in Table 5, CroPe reduces training time by 52%–64%, lowers memory usage by 28%–64%, and improves inference speed by approximately 45%. These results demonstrate that CroPe not only avoids introducing significant complexity but also enhances efficiency through a streamlined training pipeline, outperforming vision-based methods that depend on high-resolution training and additional forward-propagation strategies.\n \n2) **Regarding manual prompts and generalization:** Manual prompts are not inherently tied to specific task/domain, and they do not affect generalization.\nFirst, we clarify that manual prompts serve as auxiliary supervision signals, constraining learnable prompts during training to ensure generalization, as they tend to overfit the source domain (labeled) rather than the target domain (unlabeled) in the task setting. Manual prompts are not used during inference, thus avoiding heavy reliance.\nSecond, in Table 9 of our paper, we compare multiple handcrafted prompts, including generic \"a photo of a [class]\" and the more scene-specific \"a typical driving scenario with a [class]\". The two prompts differ by only a marginal 0.8 mIoU compared to our overall improvement of 6.5 mIoU in ACDC-Night dataset, indicating that the generic prompt is also sufficient for achieving strong performance, without affecting generalization. We use the scene-specific prompt \"a typical driving scenario with a [class]\" because current UDA benchmarks, especially those under adverse conditions, are almost exclusively focused on driving scenes (e.g., TPAMI 2023: Domain adaptive and generalizable network architectures and training strategies for semantic image segmentation). Hence, prompts like \"a typical driving scenario...\" are designed specifically to enhance performance under these conditions. For broader applicability, prompts such as \"a photo of a [class]\" offer more general semantic coverage and can be used for non-driving domains.\n\n\n**W3:** CroPe (ViT-L/14 w/ Full) comprises 341.37M parameters, with the CLIP-ViT-L/14 backbone accounting for the majority (214.64 M). To address resource-constrained real-world scenarios, we provide a lightweight variant CroPe(ViT-L/14 w LoRA) with only 64.70M parameters, lower than 85.69M of MIC, while maintaining excellent performance (71.4 mIoU). As shown in Table 5, this LoRA-based variant also offers significant efficiency benefits. It reduces training time by 56% (10 hours vs 23 hours of MIC), lowers GPU memory usage by 49% (12.0 GB vs 23.5 GB), and improves inference speed by 43% (2.60 images per second vs 1.82 for MIC). These results demonstrate that CroPe(ViT-L/14 w Full) is suitable for high-precision scenarios, while the LoRA variant offers an efficient, low-resource alternative for applications requiring high real-time performance, effectively balancing performance and practicality.\n\n**W4:**  See response of **Regarding manual prompts and generalization:** in **W2**.\n\n**Q1:** Intuitively, incorrect fixed prompts may impose misguided constraints on learnable prompts. To validate this, we conducted experiments comparing three settings: incorrect prompts, no prompts, and correct prompts.\n| Prompts | ACDC-All | ACDC-Night | ACDC-Fog | ACDC-Rain | ACDC-Snow |\n|-----------------------|:----------:|:------------:|:----------:|:-----------:|:-----------:|\n| a non-driving scenario with [class] | 69.1     | 50.6       | 75.3     | 69.7    | 70.8      |\n| no prompts  | 70.1     | 52.8       | 74.9     | 69.9      | 71.6      |\n| a typical driving scenario with a [class] | 71.5     | 54.2       | 78.2     | 72.1      |73.0|\n \nThe results show that incorrect prompts yield the lowest average performance, consistent with our expectations. Incorrect prompts introduce erroneous semantic priors, constraining text embeddings in unintended directions. Notably, the no-prompt setting outperforms incorrect prompts in most cases. This indicates that correctly designed prompts enable robust adaptation, even in diverse ACDC-All scenarios.\n \n**Q2:** We conduct experiments on two normal scene understanding tasks (GTA5 to Cityscapes and SYNTHIA to Cityscapes), and the relevant results are shown below. Specifically, we conducted experiments using the generic prompt “a photo of a [class]” rather than “a typical driving scenario with a [class].”\n| Methods  | Training Resolution | GTA5 to Cityscpes | SYNTHIA to Cityscapes |\n|:--------:|:--:|:-----------------:|:---------------------:|\n| DAFormer | LR(512×512) |       68.3        |         60.9          |\n|   MIC    | LR(512×512)+HR(1024×1024) |       75.9        |         67.3          |\n|  CroPe   | LR(512×512) |           74.7        |          67.6             |\n\nThe results show that CroPe achieves improvements of 6.4 mIoU and 6.7 mIoU over the baseline (DAFormer), respectively. These findings are particularly encouraging, as they highlight the robust performance of CroPe even in scenarios with smaller domain gaps and less severe visual degradation, where the impact of our modules may be weakened. While CroPe demonstrates strong performance in adverse scenarios, these new experiments further validate the potential and scalability of the prompts in other scenarios.\n \n**Limitations:** Thank you for pointing out these comments. \n1) Please see **W3** for parameter details.\n2) Please see **W2** for manual prompts details.\n3) Regarding limitations in unknown environments: In Unsupervised Domain Adaptation (UDA) tasks, model transfer performance is typically evaluated using the intersection of source and target domain category sets. CLIP currently exhibits strong expressive capability for these categories, meeting task requirements. To further clarify, we will discuss more potential limitations in the revised manuscript.\n \n**Paper Formatting Concerns:** Thank you for pointing out the formatting issue. Upon careful review, we found that the image resolution was reduced during the conversion process, resulting in unclear visual quality. We have re-exported high-resolution versions of the affected figures and will update them in the revised manuscript to ensure clarity."}, {"review_id": "Oymt1o9WWZ", "reviewer": "Reviewer_p1zo", "summary": "This paper proposes a framework CroPe to incorporate text modality for scene understanding in adverse weathers. The model augments visual features with text through a multi-scale visual feature extractor together with multi-level text embeddings to extract features useful for semantic segmentation. The authors have conducted extensive experiments in various weather settings and with different training schemes to demonstrate the effectiveness of the framework.", "questions": "1. Can the authors provide more clarification on the term Cross-Modal Semantic Compensation? At inference time, how is the handcrafted text prompt being generated? Is the text prompt tied to fixed scene assumptions (e.g. driving) and how does this affect generalization?\n2. The paper claims that previous methods tend to have hallucinations at several places. What are the intuitions behind this and are there any specific references that discuss this issue? Also how does the author quantitatively compare hallucination of the proposed model versus the baseline models?", "limitations": "The authors mentioned one limitation of increased model parameters. More discussion can be included on potential generalization challenges (e.g., unseen categories or scene types) or the dependency on prompt engineering.\n\nSuggestions:\n1. Table and Figure captions in the experiment section can be improved by providing a concise summary of the main observations. For instance, what is the reader expected to see from the feature visualization in Figure. 5? \n2. Given the moderate size (parameter count) of the model is around ~100 to 300M I think it is not particularly informative to include LoRA experiments as they are usually applied for much bigger pre-trained models. Most of Table 5. could be moved to Appendix. \n3. Is there any ablation results on the effectiveness of the orthogonality constraint?", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n1. The methodology figure (Fig. 2) is well-made and clearly demonstrated the key designs of the proposed framework.\n2. Experiments contain comprehensive comparisons with various baseline models and under different adverse weather settings.\n\nWeaknesses:\n1. The main contribution has limited novelty. The model incorporates CLIP text embeddings with feature pyramid network (FPN) which is common for semantic segmentation and detection models since MaskRCNN. The main experiments is on how to incorporate those text embeddings into the network through common attention/fusion operations.\n2. More explanations of the learnable and handcrafted text prompts are needed as they serve the core component of this model. For instance, the descriptions on handcrafted prompts can be moved to the main paper instead of in the appendix only. \n3. The experiment section need to contain more in-depth discussion of why the proposed model outperforms similar prior work and does not need to re-iterate the quantitative performance gain as they are already clear in the table. The term 'hallucination' occurs frequently as weaknesses of existing baselines but is not quantitatively measured.", "was_revised": true, "final_justification": "The authors' rebuttals and responses have addressed some of my concerns. However, I think the paper overall would benefit from more editing to improve the clarity of the method, with clearer motivations for each introduced module (especially the design of text prompt related modules).", "rebuttal": "Thanks for reviewing our work and providing these valuable suggestions and comments.\n \n**W1:** While our method leverages commonly known components such as CLIP text embeddings and FPN, we emphasize that their integration to compensate for severely degraded visual features (e.g., night, fog) is novel in unsupervised domain adaptive semantic segmentation (UDASS).\n\nFirst, to the best of our knowledge, our work is the first to apply CLIP text embeddings in unsupervised domain adaptive semantic segmentation (UDASS) to compensate for domain gap caused by severe visual degradation. In contrast, existing CLIP-based methods, such as WeakCLIP [1] and TP-SIS [2], typically generate segmentation predictions directly from vision-language score maps, facing challenges in UDASS due to insufficient visual features and limited prompt expressiveness. Our approach introduces a decoupled prompt design that separates domain-invariant and domain-specific text embeddings, coupled with a gated complementary fusion module to dynamically integrate domain-specific and domain-invariant representations. This design surpasses conventional attention/fusion operations, significantly enhancing the adaptability of CroPe to complex domains.\n\nSecond, our method achieves a 6.5 mIoU improvement on the CS to ACDC-Night dataset (Table 1) while also improving training and inference efficiency (Table 5). These results demonstrate the practical value and effectiveness of our proposed design.\n\nIn summary, our unique perspective, task-specific design, and substantial performance gains represent a significant and non-trivial contribution. We will emphasize these points more clearly in the revised manuscript.\n\n[1]WeakCLIP: Adapting CLIP for Weakly-Supervised Semantic Segmentation, IJCV 2025.\n[2]Text Promptable Surgical Instrument Segmentation with Vision-Language Models, NeurIPS 2023.\n\n**W2:** As described in the paper (Page 3), the learnable prompt is integrated into the CPTG and RCTVF modules, providing semantic compensation for severely degraded visual features and serving as the core prompt of the model. The handcrafted prompt acts as an auxiliary supervision signal during training, enhancing the generalization of the learnable prompt and preventing overfitting to source domain scenarios. Due to page constraints in the initial submission, some details are placed in the appendix. In the revised manuscript, we will integrate these critical details into the main text to highlight their central role. Thank you again for your insightful suggestions.\n\n \n**W3:** The experiment section is described to provide clear and objective final performance metrics, enabling readers to quickly understand the performance of CroPe. Regarding the depth analysis of the reasons behind performance improvements, we have primarily presented these through the ablation study. For instance, switching to the CLIP encoder leverages its larger pre-trained knowledge to enhance expressive capacity (Table 8); discarding ImageNet feature distance regularization allows the model to focus more on specific scenes, as the CLIP encoder does not rely on retaining ImageNet pre-trained knowledge (Table 8); the RCTVF module, through semantic compensation, outperforms existing vision models even with pure visual input (Table 10); and CroPe achieves higher training efficiency with smaller resolutions and fewer computational strategies (Table 5). To further improve, we will provide an in-depth analysis in the revised manuscript on how these designs address the extreme visual degradation challenges in UDASS.\n\nRegarding the hallucination issue, mIoU or MMD (Figure 7) metrics can be used for quantification. A detailed discussion is provided in **Q2**.\n\n**Q1:** Below is our clarification for all questions.\n\n1) **Term explanation:** Cross-modal semantic compensation refers to integrating the high-density semantics of the text modality into the visual modality to eliminate visual feature degradation in adverse scenarios (e.g., fog, night). It is well known that the visual features are sensitive to changes in illumination, texture, and occlusion patterns, while the texts providing a class inforamtion can ensure the semantic stability across different conditions and domains. The stable textual features offer semantic compensation for visual features in cross-domain settings, thereby improving the generalization ability of cross-domain sementic segmentation.\n \n2) **Explanation of handcrafted prompts at inference time:** Handcrafted prompts are not required at inference time, we only use learnable prompts. The handcrafted prompts (e.g., “a photo of a [class]”) are only used during training as generalization constraints to regularize the learning process and prevent the learnable prompts from overfitting to the source domain (e.g., Cityscapes), thereby improving generalization to target domains.\n\n3) **Fixed scene assumptions:** Text prompts are not inherently tied to scene assumptions, and they do not affect generalization. Specifically, in Table 9 of our paper, we compare multiple handcrafted prompts, including generic \"a photo of a [class]\" and the more scene-specific \"a typical driving scenario with a [class]\". The two prompts differ by only a marginal 0.8 mIoU compared to our overall improvement of 6.5 mIoU in ACDC-Night dataset, indicating that the generic prompt is also sufficient for achieving strong performance, without affecting generalization. We use the scene-specific prompt \"a typical driving scenario with a [class]\" because current UDA benchmarks, especially those under adverse conditions, are almost exclusively focused on driving scenes (e.g., TPAMI 2023: Domain adaptive and generalizable network architectures and training strategies for semantic image segmentation). Hence, prompts like \"a typical driving scenario...\" are designed specifically to enhance performance under these conditions. For broader applicability, prompts such as \"a photo of a [class]\" offer more general semantic coverage and can be used for non-driving domains.\n \n4) **Prompt generalization:** We conduct experiments on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes datasets, both of which represent normal scenes rather than adverse scenes, to evaluate the generalization ability of the “a photo of a [class]”.\n| Methods  |    Training Resolution    | GTA5 to Cityscpes | SYNTHIA to Cityscapes |\n|:--------:|:-------------------------:|:-----------------:|:---------------------:|\n| DAFormer |        LR(512×512)        |       68.3        |         60.9          |\n|  CroPe   |        LR(512×512)        |       74.7        |         67.6          |\n \nThe results show that CroPe achieves improvements of 6.4 mIoU and 6.7 mIoU over the baseline (DAFormer), respectively. These results highlight robust performance of CroPe, even in scenarios with smaller domain gaps and milder visual degradation. The prompt \"a photo of a [class]\" also maintains good generalization. \n \n**Q2:**  Below is our clarification for all questions.\n\n1) In this paper, hallucinations refer to models erroneously predicting non-existent environmental elements based on prior knowledge. For example, due to the high joint probability between “road” and “sidewalk,” a model may incorrectly predict sidewalks alongside a “road” where none exist. In adverse UDASS scenarios, such hallucinations are more severe. Adapting to multiple adverse conditions (e.g., ACDC-Fog, ACDC-Night) requires learning joint distributions for each source-target domain pair, yet knowledge from foggy conditions (e.g., ACDC-Fog) often proves unreliable in nighttime settings (e.g., ACDC-Night), leading to mispredictions. ProMaC [1] has clarified this hallucination problem, while MCGDA [2] and Fifo [3] further note that significant texture and spatial structure variations in nighttime and foggy conditions aggravate erroneous predictions.\n \n2) The hallucination can be quantified by UDA performance (e.g., mIoU) or by assessing enhanced class distinguishability using the MMD metric (Figure 7). \n\n[1] Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation, NeurIPS 2024.\n[2]Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation, TPAMI 2020.\n[3] Fifo: Learning fog-invariant features for foggy scene segmentation, CVPR 2022.\n  \n**Limitations:** Thank you for your valuable suggestions. We will discuss further potential challenges with generalization or limitations of the model in the revised manuscript.\n \n**S1:** We will revise the captions of figures and tables to summarize key observations concisely.\n \n**S2:** In the revised manuscript, we will move Table 5 to the Appendix and include additional analysis of key modules to emphasize core experimental results.\n \n**S3:** The orthogonal loss $L_{orth}$ is a pivotal component of our decoupling strategy. When mapping text embeddings $T$ with MLPs, $L_{orth}$ is essential by default, as its absence prevents effective decoupling. Thus, no separate ablation was needed. To address your concern, we conducted additional experiments confirming the critical role of $L_{orth}$ in decoupling. \n| Methods | CS to DZ | CS to ACDC-All | CS to ACDC-Night | CS to ACDC-Fog | CS to ACDC-Rain | CS to ACDC-Snow |\n|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n| CroPe |  48.7|  71.5| 54.2 | 78.2 | 72.1 | 73.0 |\n| w/o $L_{orth}$ | 47.7 | 68.7 | 51.3 | 73.6 | 68.6 | 67.9 |\n \nThe results show that removing $L_{orth}$ leads to a 2.8 mIoU drop on the ACDC-All dataset, as MLPs fail to achieve decoupling. We will incorporate these analyses into the revised manuscript."}]

Author rebuttal:

Meta-review: This paper proposes a cross-modal semantic compensation adaptation method for robust scene understanding in adverse conditions (fog, snow, and night). Existing approaches only rely solely on visual information, CroPe introduces textual information to provide consistent representations. CroPe includes CPTG to provide text embeddings, RCTVF to conduct cross-modal compensation. Experiments show the effectiveness.

Strength:
- CroPe introduces textual information to enhance performance in adverse conditions, and experiments show the effectiveness.
- The experimental results are decent.
- The paper is well-written and easy to follow.
- The authors provide additional quantitative results to deal with questions raised by reviewers, including ablations, encoder vs. decoder in the domain-specific perception module, and experiments with different prompts.

Weakness:
- The core idea and innovation should be discussed more in the Introduction section, as many reviewers raise questions about the novelty.
- The mentioned experiments during the author rebuttal phase should be added in the revised version, i.e., experiments on normal scenarios.
Decision: Accept (poster)