Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation
Authors: Wangkai Li
Rui Sun
Huayu Mai
Tianzhu Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Semantic segmentation
unsupervised domain adaptation
Primary area: applications

Abstract: Semantic segmentation suffers from significant performance degradation when the trained network is applied to a different domain. To address this issue, unsupervised domain adaptation (UDA) has been extensively studied. 
Despite the effectiveness of selftraining techniques in UDA,  they still overlook the explicit modeling
of domain-shared feature extraction.
In this paper, we propose DiDA, an unsupervised domain bridging approach for semantic segmentation.   DiDA consists of two key modules: (1) Degradation-based Intermediate Domain Construction, which creates continuous intermediate domains through simple image degradation operations to encourage learning domain-invariant features as domain differences gradually diminish; (2) Semantic Shift Compensation, which leverages a diffusion encoder to  disentangle and compensate for semantic shift information with degraded time-steps, preserving discriminative representations in the intermediate domains.
As a plug-and-play solution, DiDA supports various degradation operations and seamlessly integrates with existing UDA methods. Extensive experiments on multiple domain adaptive semantic segmentation benchmarks demonstrate that DiDA consistently achieves significant performance improvements across all settings.
Code is available at https://github.com/Woof6/DiDA.

Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation
Wangkai Li', Rui Sun', Huayu Mai', Tianzhu Zhang
MoB Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University Science and Technology of China
National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory (lwklwk, issunrui, mai556)@mas 0mail. ust..stteee edu. cn, tzzhangdustc. edu. cn
Abstract
Semantic segmentation suffers from significant performance degradation when the trained network applied a different domain. To address this issue, unsupervised domain adaptation (UDA) has been extensively studied. Despite the effectiveness of selftraining techniques in UDA, they still overlook the explicit modeling domain- shared feature extraction. In this paper, propose DiDA, unsupervised domain bridging approach for semantic segmentation. DiDA consists two key modules: (1) Degradation-baan Intermediate Domain Construction, which creates continuous intermediate domains through simple image degradation operations features domain differences gradually diminish; (2) Semantic Shift Compensation, which leverages a diffusion encoder disentangle and compensate for semantic shift information with degraded time- steps, preserving discriminative representations in the intermediate domains. As a plug-and-play solution, DiDA supports various degradation operations and seam- lessly integrates with existing UDA methods. Extensive experiments on multiple domain adaptive semantic segmentation benchmarks demonstrate that DiDA con- sistently achieves significant performance improvements across all settings. Code is available https: /// //i/ thub. com/Wooff/DiDA.
encourage learning
Introduction
Semantic segmentation, fine- graiinr pixel-wise classification task, assigns semantic class labels to each pixel, facilitating high-level image analysis. Despite the remarkable progress made in this field [56, 6, 18, 17], networks trained within source domain often encounter significant performance degradation when applied to target dataset due to domain discrepancies. Mitigating this issue to enhance the generalization capability of networks remains a formidable challenge. address this problem, extensive research has resorted to unsupervised domain adaptation (UDA), which aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
In previous works, to fully exploit the abundance of unlabeled target domain data, self-training techniques have been naturally incorporated into UDA tasks and have emerged paradigm. The core idea of this paradigm lies in constructing a teacher network via temporal ensembling mechanisms, which generates pseudo-labels by predicting the target domain images. These pseudo-labels are then used to progressively guide the student network's 's learning on the target domain. Despite achieving impressive results, these methods still overlook the explicit modeling of domain-shares feature extraction, which remains a central challenge in UDA. To illustrate this, we refer the classic notion of causal representation learning [36, 74]: any observed feature can
mainstream
Coorrssponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Plnc groeegra Domall specille atteibute loct
increasingly ovestopping arca
Coarse Seaeze-graieddDDaa Invariaut otceibute lost
Image Degradation
X
X
X
Domatt Distrirution
Initial Domains
Intermedinnte Dumaino (k)
Intermedinte Dumaino (tg)
Intermodiate Domnina (T)
Source Domodn
Terget Dumnii
Overlapping Area
V Image Sample
Noise Ditribtion
Amhigumm Recomernation
Figure I: Conceptual illustration of the diffusion forward process. Fine-grained, domain-spechie attributes such as texture are lost with less noise added (i.e., early time-steps), while coarse-grained, domain-invarian ones such as shape are lost by adding more noise (i.e., late time-steps).
be generated as = ((c, e). where & is a generator, denotes the causal feature that determines the domain-invariant class identity (c.g., shape), and represents the environment-specifie feature (e.g., texture). Since environmental features are domain-specific, the domain shift éş * er leads to as # Fr, thereby hindering the learning of a truly domain- invarinnn class representation.
In this work, we explore a novel perspective for unsupervised domain bridging and demonstrate that simple image degradation operations can serve as effective priors. Our key idea is motivated by the theoretical insight (Sec.3.2) of the Denoising Diffusion Probabilistic Model (DM) [37,76]. As shown in Fig. the forward diffusion process incrementally adds Gaussian noise input samples at each time step. This process gradually removes domain dopeiin-peccfiic attributes, effectively collapsing samples from different domains into a shared representation space. As the time step increases, this diffusion process enlarges the overlapping area of the probability density functions of the noisy domain distributions. Existing studies [95, 77] show that this overlapping area closely correlated with the DM's s ambiguous reconstruction of different samples. For instance, due to the large overlap between the intermediate distributions at a certain time step fzz, noisy samples drawn from these may be reconstructed ambiguously as either source domain or target domain. This observation leads to a crucial insight: the overlapping area formed through degradation can be interpreted as domain- shared distribution, which acts as a valuable prior for learning domain-invariant representations. This hypothesis is further substantiated by our quantitative analysis presented in Appendix H. where we empirically validate the correlation between degradation level and domain alignment effectiveness.
is non-trivial to interpret image degradation as form of domain bridging, especially considering its fundamental differences from consistency regularization based on data augmentation [40, 93]. This perspective introduces two major challenges: (1) Wide Range of Degradation Levels: Degradation operations, when applied incrementally from mild to severe, can gradually eliminate domain-specttn attributes. To effectively capture domain- invariant information under such conditions, the encoder of the segmentation network should maintain stable and consistent feature representations across varying levels of degradation. (2) Semantic Shift Due to Feature Corruption: While degradation helps remove domain-specific cues, it also inevitably affects domain-invariant features. This degradation of essential semantic information hinders the encoder's s ability to extract discriminative representations, which in turn compromises the learning process of the segmentation head. This issue is commonly referred to as the semantic shift problem [1, 86].
To this end, we propose an unsupervised domain bridging approach for semantic segmentation, termed DiDA, which constructs intermediate domains via image degradation defined by forward diffusion process. We introduce the following key components to integrate DiDA into the UDA training pipeline: (1) Degradation- -bsed Intermediate Domain Construction: Based on the definition of the forward diffusion process, we construct sequence of continuous intermediate domains through simple image degradation operations. These intermediate domains are incorporated into the UDA training process to encourage the model to learn more comain-invariant features from the increasingly

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overlapping area of domain distributions. (2) Semantic Shift Compensation: To mitigate the semantic shift problem introduced during intermediate domain construction, we propose diffusion encoder, conditioned on a time embedding module. This encoder disentangles the time-specific semantic shift and, through residual connections across multiple feature levels, compensates for the lost discriminative representations in intermediate domains. This ensures better semantic alignment between extracted features and corresponding labels. (3) Expansion to Arbitrary Degradation: To demonstrate the flexibility of our framework, we explore various degradation operations for constructing intermediate domains. The implementation of DiDA is compatible with any image degradation method by simply replacing the Gaussian noise addition in the standard diffusion process. This characteristic showcases the generality and extensibility of our framework. Our approach can be regarded as plug-an boug-and-pul and-play training strategy, which can be seamlessly integrated with various UDA methods and network architectures, consistently yielding performance improvemenss.
In this work, our contributions can be summarized as follows: (1) We propose novel domain bridging mechanism based on image degradation to facilitate the learning of komain-invariaan features. This introduces new perspective for domain-adaptiv semantic segmentation. (2) We design unified framework that integrates diffusion strategies into the training pipeline of UDA. By introducing series of degradation-based intermediate domains, our approach enables progressive learning of invariant representations and effectively mitigates the semantic shift problem commonly observed in intermediate domains. (3) We validate the effectiveness and versatility of our approach through extensive experiments on multiple UDA methods, benchmarks, and network architectures. DiDA consistently achieves significant performance improvements across all settings.
2 Related Work
2.1 Unsupervised Domain Adaptation (UDA)
Unsupervised Domain Adaptation (UDA) aims to transfer semantic knowledge from labeled source domains to unlabeled target domains. Given widespread domain gaps [88, 67]. UDA has been extensively studied across various vision tasks, including image classification [29, 28, 57, 35, 34], object detection [11, 12, 51], and semantic segmentation [83, 101, 98, 9]. Semantic segmentation requires assigning label to each pixel, which often leads to high annotation costs [13, 52, 87]. To address this, various lahel-efficient methods have heen developed, such as semi-supervised learning [79, 62, 78], few-shot learning [54, 59, 55, 53], and domain adaptation [49, 15, 14]. Among them, UDA is particularly valuable, as it eliminates the need for costly pixel-level annotations in new domains. Recent UDA methods for semantic segmentation fall into two main categories: adversarial training and self-training. Adversarial methods align source and target feature distributions via min-max game between feature extractor and domain discriminator [81, 83]. Self-training methods, gaining traction due to the framework to generate pseudo labels for target images [82, 38, 60, 50]. Their success hinges on producing reliable pseudo labels through entropy minimization [8]. consistency regularization [40], and blassealaaceed training [48]. Our method builds upon the self-training paradigm, introducing a novel domain bridging mechanism via image degradation to enhance domain invariance and improve adaptation performance.
robustness of Transformers [4]. adopt teacher-student
2.2 Generative Models for Segmentation
Generative models like GANs [31] and VA [45] map data to latent codes following simple distribu- tions (c.g., Gaussian), enabling data generation and manipulation. Denoising Diffusion Probabilistic Models (DDPMs) [37] extend this by modeling the data-to-latent mapping as Markov chain with in- termediate distributions. To adapt such generative mechanisms semantic segmentation- a typically discriminative task- task-some works synthesize paired images and labels to train separate segmentation networks [47, 100], while others directly exploit internal features from generative models [91, 3, 80]. In domain adaptive segmentation, diffusion based approaches have shown promise. Some methods leverage style transfer [68, 69) or generate diverse domain samples to enhance generalization [66, 99], while others estimate segmentation uncertainty to guide sample selection [26]. In contrast, our method integrates diffusion strategies into the UDA self-training framework, progressively learning domain-invarian representations from images sampled across intermediate distributions.
3

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3 Methods
In this section, we first formalize the standard self-training paradigm for UDA and the training process for diffusion models, respectively (S) Then, we introduce theoretical insight (Sec.3.2), After that, we describe our new UDA framework, DiDA, a novel perspective for domain bridging that couples diffusion strategies to improve UDA semantic segmentation performance (Sec.3.3). Finally, we illustrate the details that expand our method arbitrary choices of image degradation (Sec.3.4).
3.1 Preliminary Knowledge
Self- Seff-Trainig (ST) for UDA. For domain adaptive semantic segmentation, the source domain can
be denoted as D. { ur ( ) where x² E Xs represents an image with vf E Ys as the corresponding pixel-wise one-hot label covering C classes. The target domain can be denoted as D; [<x]) which shares the same label space but has no access target label Y In this setting, the supervised loss L can be calculated on the source domain to train neural network fe:
No
Lae Can (fo(x))) 1.
(1)
where Lee denotes the pixel-wise cross-entropy loss:
HxW
Lee vi. = Σ
Σ (as.)) log ((...))
(2)
1=1
Self-training introduces a teacher-student framework to generate pseudo-labels p" for the target domain (see Fig. 2): P(....e) [e = argmax, fo(a fo(=))..e)) (,e')), where La is the teacher network. Then,
the pseudo-labels are used to train the network fo on the target domain with the adaptation loss L
Nr Lee (fo(x))) .T))
The quality of pseudo-labels is weighted by confidence estimate q" [38], which gradually strength- ens with increasing accuracy of models. After each training step, the teacher model fs is updated with the exponentially moving average of the weights of fa. The segmentation model, fa, can be defined as fa h g. where g X Z is an encoder that lifts each pixel of the input image in X' to the feature space 2, and h Z + RC is a segmentation head which can be viewed as pixel-wise classifier to give a score for each class.
Diffusion Model. Diffusion models learn a series of state transitions to generate high-quality sample from the noise, defined forward diffusion process during the training phase. The diffusion process generates intermediate state Iy with a random uniformly sampled t from []...,T]:
g(>x(x0)= = (2 v Va,aa, (1 - G.).),
(4)
where de originates from predefined noise schedule (decreases from 1 to 0). This process can be rewritten through a reparameterization trick:
Ft Vapz VT VT- ãie, e~11001).
(5)
Afterword, a network trained to predict noise e (or predict sample data 2o directly) from Iy, with a reconstruction loss:
c [1fe(zs, el12.
(6)
3.2 Theoretical Insight
In the forward diffusion process, the loss of attributes with different levels granularity is directly related to the time step. Specifically, we restate following proposition based the findings in [95]:
Proposition (Attribute Loss and Time Step). 1) For each attribute Z,, there exists a minimum time step ((z)) such that Z, is lost with degree r at every E (I(Z)).... T). 2) There exists set (8 such that (Z,)) > ((2z) whenever the distribution of || - 9t xo.l first-order stochastically dominates that of | gj æoll. where 20 X' uniformly.

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image
Tarper Image
Tuccler Not. J
Pacudo
Stop Grallent
agmeniaiion lesel
Skadeent Not. Je
Adspeaation Laxa e
Degrodation -aared Intermediate
cuouder
agueciaaion lead. B
Supervieed Loss. C²
Domaii Construction
Intermediate froFuces. 2n
Semaxtic
Comppeseation
stoodeer.
Reosnstrartton Lowe
fanget domada
Intecmediate domain
Figure 2: Overview of DiDA framework. We integrate diffusion strategies (green box) with standard self-training paradigm. While regular frameworks train networks using supervised loss on source domain and unsupervised adaptation loss target domain, DiDA introduces regradation-basee intermediate domains and addresses semantic shift through a diffusion encoder and reconstruction head, which are enabled by degraded image consistency (DIC) loss and reconstruction loss.
The first part of the proposition indicates that once an attribute Z: is lost at certain time step ((Z)) with a specified degree т, cannot be recovered subsequent steps. The second part implies that if the i-th modular attribute transformation 9; induces larger changes in pixel space than 9j. then the corresponding attribute Z; (typically a searse-grained attribute) is lost at a later time step than Zj (a fine-grained attribute). This theoretical result reveals that in the forward diffusion process, fine-grained attributes (e.g., texture) are lost earlier than coarse- granneeg ones (e.g., shape), with granularity measured by the magnitude of pixel-level changes induced by modifying the attribute.
This proposition also shows the overlapping area between domains closely related to the DM's inherent ambiguity in reconstructing degraded samples. Based on this insight, we reinterpret the overlapping area-created by predefined image degradation- as a domain-sharre distribution. This serves valuable prior, enabling the network to extract domain-invarian representations.
3.3 DiDA Framework
Although intermediate domains generated via image degradation can facilitate cross-domain adapta- tion, they should contend with wide range of degradation levels and alleviate the risk of semantic shift. To overcome these challenges, we propose DiDA with two key modules: Degradation- Intermediate Domain Construction and Semantic Shift Compensation. Our framework processes images at varying degradation levels and disentangles semantic shift through a dedicated diffusion encoder and reconstruction head. Designed a general and flexible solution, DiDA seamlessly integrates with existing network architectures and UDA methods.
Degradation based Intermediate Domain Construction. Degradation operations, when applied incrementally from mild to severe, can gradually enlarge overlapping area between domain distribu- tions and eliminate domain-speciff. attributes. To effectively capture domain-invarian information under such conditions, the encoder should maintain stable and consistent feature representations across varying levels of degradation. We propose a general approach by formalizing continuous degradation operations as a diffusion forward process.
The learning objective of generative models can be framed finding transport map T Rd Rd between two distributions, i.e., = T(Z), where ~ Tz. ~ Tz. Typically, Z follows a simple elementary (Gaussian) distribution for sampling generate X from the data distribution wa. In the diffusion process, the transport map formulated as a Markov chain with learned Gaussian transitions starting at XT Nz:
XT XT-1 PotN; 11xx)
X+-1 Xo.
(7)
5

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which is termed as a reverse process in contrast to equation (4), In our case, Xo ~ {TT, Nt. represents the distribution of the source or target domain, respectively. Since X1, X2, Xy can be viewed as data Xo. we consider them as intermediate domains, which possess gradually increasing overlapping area compared to the original Xo. This formulation enables degradation-base bassd domain bridging mechanism within the diffusion framework.
latent codes of the same dimensionality as the
Semantic Shift Compensation. While degradation helps remove domain-specific cues, it also inevitably affects domain-invariant features. This degradation of essential semantic information hinders the encoder's ability to extract discriminative representations, known as semantic shift. To address this challenge, we propose a compensation mechanism that effectively disentangles the semantic shift and aligns the extracted features with label semantics throughout the degradation process, enabling the network to process intermediate domains at any degradation level while maintaining semantic consistency.
In this module, a trainable diffusion encoder, g', is introduced to map each degraded image xy to the feature space 2 given t. This diffusion encoder g' is designed to capture the semantic shift information of the segmentation network's encoder g when taking Iı as input. To this end, a time embedding module is added to specify the diffusion time t. It is implemented by the Transformer sinusoidal position embedding [84] condition all blocks of g' on t. Then, the internal feature 2(C.i) in block! is modulated with shift and bias:
i(t.a) =(1,9 (MLP's Embed(t) 1) + -LP,' Embed(t),
(8)
which is operated at the channel dimension of < The resulting feature 2, is required to compensate for the intermediate feature &, from to minimize the reconstruction error by being supervised with the loss L (see equation (6)). where fe is replaced by fo h'' o (g + g'). We perform feature fusion by adding hierarchical features through residual connections. Through this module, the network is empowered to precisely disentangle the degree of perturbation and align the extracted features with label semantics by compensating for the semantic shift. Meanwhile, it retains the capability to adapt Nomain-invarian knowledge to the original domain distribution.
To leverage intermediate domains for improved adaptation, we introduce Degraded Image Consis- tency (DIC) loss:
NT > Coelfololen 0. 1) > t.
t),
(9)
where fo ho (g + 9') and Fi, is degrade image of 2;,0, which can be obtained with equation (5). This loss enforces consistency between predictions on degraded and original images.
Training and Inference. The training pipeline is shown in Fig. 2 and detailed in Appendix A. The original operation in the self-training framework is fully retained, and DiDA can be considered as an additional plugin. In each training iteration, we conduct the forward process with fixed noise schedule āt and random sampled from a uniform distribution between 1 and on the current training batch. Then, they are fed to the network fe/ fo. which shares the same weights with student net fe, and diffusion time t is encoded through the time embedding module in diffusion encoder g'. The outputs of this step, prediction for segmentation map and noise, are individually supervised by DIC loss and reconstruction loss. The overall loss L for DiDA is the weighted sum of the presented loss components:
c= C + XDL + AnC
(10)
During regular inference, only the backbone segmentation network fe = h o is used, while the diffusion-specifie components--! g' and h'-are entirely removed. The input is an unprocessed image from Xo. and there is no need to use the diffusion encoder or reconstruction head for noise prediction, meaning that additional time consumption network structure changes required in this stage.
3.4 Expansion to Arbitrary Degradation
Diffusion models demonstrate flexibility in their choice of degradation operations beyond traditional Gaussian noise [2]. Building on this insight, we extend DiDA to support various degradation types while maintaining our core principles of intermediate domain construction and semantic shift perception. To demonstrate this generalization, we implement forward diffusion processes based

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Table 1: Quantitative results of DiDA on different methods and benchmarks with CNN-based model (C) or Transformer- based model (T). denotes the reproduced result.
Table 2: Component ablation analy- sis of DiDA built with DAFormer on GTA. CL---CS..vva
Methed
GIA -C.
SYN. CCS
CS. ACDC
2
A
mloU
1
T
68.1
DAFormer [S]]
360
68.3
54.7
60.9
55.4
06.5
38.312.3
70.3r2.0
57.612.9
63.1 2
193.7
69.5
TRRDA
63.0
15.8
61.2
65.8
65.0
07.9
-DIDA
64.3
75.4 e.
62.611
6T
70. T12-7.
69.4
[40]
75.5
67.5
69.8
69.9
-DiDA
65.040.s
78.8
63.5
68 38.8,1.
72.1
70.5
on two fundamental vision tasks: deblurring and inpainting. These implementations preserve the network's ability to perceive degradation levels and compensate for semantic shifts across different degradation operations. Please refer to Appendix for details on the implementation.
4 Experiments
4.1 Implementation Details
To comprehensively evaluate the performance of DiDA, we follow standard UDA pro- tocols and conduct experiments on both synthetic-to-real and weattt adaptation scenarios. As synthetic datasets, we use GTAv [70] containing 24, 24,966 images and SYNTHIA [71] with 9,400 images. For real-world datasets, we employ Cityscapes [21] ] with 2,975 training and 500 validation images representing clear weather conditions, and ACDC [72] containing 1,600 training, 406 validation, and 2,000 test images capturing adverse weather conditions (fog, night, rain, and snow). We report the intersection over union for each class well as the mean IoU over all classes.
Datasets.
Base Segmentation Architectures and UDA Methods To demonstrate the versatility and adaptability of our method, we implement it on two widely-used network architectures and three progressively enhanced baseline methods. Specifically, we employ DeepLabV2 [5] with ResNet- 101 [32] backbone and DAFormer [38] with MiT-B5 [90] backbone. Both of these network architectures have been pretrained on the ImageNet- 1k [24] dataset. Building upon this foundation, we apply our framework to different methods in the DAFormer series, including DAFormer [38]. HRDA [39], and MIC [40].
Training Details. We implement DiDA based on the MMSegmentation [20] framework. Depending on the complexity of the network architectures and UDA frameworks, all experiments are conducted on one or two RTX-3090 GPUs with 24 GB memory, with 40K training iterations and a batch size of 2. We train the network using the AdamW optimizer, with learning rates of 6 x 10-5 for the encoder and 10-4 for the decoder, a weight decay of 0.01, and linear learning rate warm-up strategy for the first 1.5K iterations. The EMA coefficient for updating the teacher network is set to 0.999. We set T= 100 and use sigmoid schedule [44] to obtain ā; To achieve scale and quantity matching during feature fusion, we initialize diffusion encoder / with the same structure as the corresponding the segmentation encoder, along with extra time embedding modules. For the reconstruction head g', we initialize an ASPP module [5] with linear projector. For the reconstruction loss, the clean image zo or sampled noise e is downsampled at a rate of 4x or 8x to match the input of the reconstruction head. We set DIC loss weight X to 0.5, and reconstruction loss weight X to 5 for DAFormer and 1 for DeepLab to ensure a similar gradient magnitude induced by these different components. Finally, report the mIoU using the last checkpoint of the student model fo without model selection.
4.2 Evaluation on Benchmark Datasets
Overall Quantitative Results. Tab. 1 shows quantitative results by building DiDA upon three mainstream methods with different architectures. We report results based on whole inference on DAFormer and slide inference on HRDA and MIC without any other test time augmentation strategies for a fair comparison. Whether with Transformer-baas based or CNN-based backbones, DiDA achieves consistent gains beyond all baselines, ranging from 0.8% to 3.7%. As expected, the performance improvement generally decreases when the baseline stronger and closer to performance saturation. When implemented with Deepl abb2, the performance gain is slightly lower than DAFormer due to the coarse output (8x downsampling) for the optimization goal of reconstruction. It is worth noting that DiDA achieves new ofte--ofthheart performance when applied on MIC.
7

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Table 3: UDA segmentation performance on GTA. where the improvement by DiDA is marked as bold. The results are acquired with ENN-based model (C) in the first group or transformer-basee model (T) in the second group. * denotes the reproduced result.
Sidewalk
Rood
Woll
Person
Bus
Trann
Motor
Method
Arch.
mlo
DACS [12]
82.9
39.7
87.9
39.7
39.5
58.5
88.0
44.0
67.2
35.8
84.5
50.2
0.2
27.3
12F [6]]
90.8
48.7
85.2
30.6
28.0
33.3
46.4
2010
85.6
39.1
88.1
61.8
35.0
86.7
46.3
55.6
11.6
44.7
54.3
53.3
PmnDA
87.8
56.0
79.7
46.3
64.8
45.6
53.5
53.5
88.6
45.2
82.1
70.7
39.2
55.8
45.5
50.4
1.0
48.9
56.4
57.5
DAP [42]
94.5
29.8
47.5
89.5
73.6
58.6
0.0
CPSL133 [S5]
92.3
59.5
45.7
29.7
52.8
87.9
73.0
35.5
90.4
73.9
26.3
53.8
MIC [10]
96.5
74.3
90.4
47.1
42.8
50.3
61.7
62.3
90.3
49.2
90.7
77.8
53.2
93.0
66.2
68.0
6.8
38.0
60.6
61.2
+DiDA
96.6
74.6
89.2
47.5
44.2
50.0
61.2
50.6
90.4
51.9
91.8
76.5
538
93.5
67.1
63.7
588
50.0
66.7
65.0
Transi3X [oo]
94.7
00.2
48.1
43.5
30.1
40.5
90.4
70.7
47.0
92.5
47.0
ADMooree [38]
96.7
75.1
88.8
57.5
45.9
25.6
55.4
50
90.2
45.6
92.1
70.8
43.0
91.0
78.9
79
68.7
52.7
65.0
69.2
[oPT]63]
97.66
901.9
91.6
62.1
55.9
59.3
66.7
70.5
91.9
55.0
94.4
80.0
55.6
94.7
87.1
88.6
82.1
65.0
68.8
76.1
Dorifrrnnr [38]
95.7
66.4
35.5
26.1
29.6
36.8
39.
69.9
47.5
92.3
'2222
'42.7"
'9223
'14'3
'662
65.1
5559
6618
663'
+JST [25]
85.3
67.7
89:3
55.5
47.1
50.1
57.2
58.6
89.9
51.0
92.9
72.7
46.
92.5
78.0
B1.6
74.1
57.7
62.6
69.3
(B)
97.2
76.3
89.2
58.0
51.1
53.6
57.5
63.5
90.0
51.3
92.3
71.7
43.
92.1
69.
K1.4
72.1
56.0
64.6
70.0
+DDAMM
96.4
75.3
90.5
57.6
49.2
53.4
64.4
90.5
52.6
92.5
71.8
40.8
91.6
70.6
81.7
66.1
57.6
69.8
+DiDA
54.
49.8
71.5
50.
K2.1
70.7
HRRDA [19]
96.4
14.4
61.0
61.6
51.5
57.1
65.0
60.
91.
48.4
942
750
52.9
93.9
6411
K5
759
63.9
67.5
-ISIA [25]
97.0
7866
91.3
60.
56.7
56.5
64
6999
91.5
50.
93.7
79.2
55.2
93.7
76.3
86.9
778
63.7
65.8
74.3
+DDDA
55.7
70.3
92.0
55.5
86.
9488
75.4
Mik (10y'
614
M0.1
91.3
61.4
36.9
60.3
66.4
113
91.3
512
193
55.6
96.6
R3
683
1443
64.7
66.1
'153'
+DTS (43)
97.0
80.4
91.8
60.6
58.7
61.7
7.9
73.2
92.0
45.4
94.3
813
58.7
95.0
87.9
90.7
82.
65.7
69.0
76.5
+DIDA
97.9
62.0
57.7
60.5
76.6
92.3
544
94.7
90.4
818
76.8
Class-level Comparison. We further display the class-wise performance on each benchmark in Tab. 3 and Tab. 4, with additional results provided in Tab. (Appendix D), for detailed comparison. When combined with DiDA, most of classes achieve higher accuracy. We investigate that performance on road, sidewalk, fence, and terrain achieves consistent and relatively significant improvements over all UDA methods and datasets. These classes constitute the main scene and comprise abundant Gomain-speciffe texture information. By adding random Gaussian noise to images, these textures broken while the context information is preserved, which is more robust over domains. Since DiDA establishes domain bridging through these progressively intensifying degraded images between domains, the network can focus more on Furthermore, we implement the extended version of DiDA on the DAFormer baseline with Blur and Mask, denoted as B and M, respectively. These extensions obtain less gain than the default version but show a certain potential if well-designed.
context to enhance the adapting ability.
Comparison with other plug-in methods. We further compare DiDA with other plug-in approaches, such as FST [25], DiGA [75], and DTS [43]. Our method consistently demonstrates superior performance compared to these methods when built with the same baselines.
4.3 Diagnostic Experiments
Please refer to the Appendix D-L for further analysis, where we provide more experiment results, deeper ablation studies, and more visualization.
Component Ablation Analysis. To gain deeper insights, we analyze the proposed approach by ablating the components and evaluating the performance with DAFormer the baseline GTA-CCS. The results are presented in Tab. 2. The DiDA with complete components achieves a 2.0 mloU gain on this baseline. To analyze the diffusion encoder and the module of time embedding respectively, we use the segmentation encoder directly conditioned by time embedding as Otime without an extra diffusion encoder, the baseline for ablation studies (row 6). We first ablate the module of time embedding, implying that the original network is equivalently trained with the randomly degraded images as additional data augmentation. The performance decreases heavily in this case (row 2) due to the excessive degradation of data, known as semantic shift problem. The introduced time embedding plays vital role in encoding noise intensity information, indicating that we alleviate this issue by encoding time-specific semantic shift information. Then, we discard the loss terms, i.e., _ and RR, respectively (rows and 4). Without LR, DiDA obtains less improvement, although does not constrain segmentation results directly, indicating that L helps to learn the module of time embedding and perceive the semantic shift. Discarding _ brings an accuracy drop since introducing L only is inconsistent with the learning objective for high-level semantics. After that, the extra reconstruction head 1 is ablated (row 5), which means that we obtain reconstruction results from the segmentation head h. It is also essential to avoid excessive disturbance for originally learned
8

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Table 4: UDA segmentation performance on SYN.+ where the improvement by DiDA is marked as bold. The results are acquired with CNN-based model (C) in the first group or Transformer- model (T) the second group.
Bullding
Road
Wall
Fense
Pole
Light
Veg
Sky
Took
Mothed
Arch
mioo
DACS [83]
30.6
25.1
29
22.7
340
K3.7
50.8
67.6
38.3
23.5
47.6
48.3
12F [o1]
$4.9
44.7
X2.2
9.1
1.9
36.2
42.1
40.2
83.8
54.2
08.9
35.3
83.0
49.8
30.1
52.4
51.8
ProDA [98]
57.8
45.7
54.6
37.1
0.6
44.0
54.6
37.0
53.1
84.4
74.2
24.3
58.2
51.1
40.5
45.5
55.5
54.2
0.2
53.6
89.3
34.6
60.5
59.8
CPSL [BB]
87.2
85.5
0.3
47.7
57.4
57.8
59.5
32.0
90.6
50.
59.8
57,9
TansDA [10]
T
90.4
54.8
56.4
31,1
1.7
53.8
61.1
37.1
90.3
92.0
71.2
25.3
92.3
66.0
44.4
49.8
59.3
ADFomer [3]]
T
91.8
53.6
$7.0
40.5
5.2
46.8
52.1
54.9
83.4
92.6
72.5
45.7
86.1
61.6
50.4
64.4
62.1
[63]
67.0
65.0
67.4
bARomer [S]
T
84.5
40.7
$9.4
41.5
6.5
50.0
55.0
54.6
96.0
89.8
13.2
48.2
K7.2
$3.2
53.9
61.7
60.9
[25]
T
58.3
46.1
S8.0
41.7
7.3
50.1
53.6
52.5
S7.4
91.5
73.9
48.1
55.3
58.6
55.9
634
61.9
-DNDA
87.8
9.8
56.8
46.5
63.1
HRDAA [S]
T
33.2
47.7
85.8
19.3
4.8
57.
65.1
60.9
92.9
19.
31.8
39.0
61.7"
63.9
61.
63.8
+DIGA [75]
T
88.5
49.9
90.1
51.4
6.6
55.3
64.8
62.7
88.2
91.5
78.6
51.8
59.5
62.2
61.0
65.8
66.2
+DDA
$7.9
59.6
11.6
56.6
6388
87.6
94.1
79.9
54.2
62.3
67.8
[10]
T
86.6
89.1
78
66.7
S7.1
94.6
58.9
90.1
67.1
64.3
67.3
+TS [43]
T
89.1
54.9
89.0
10.1
8.
61.6
67.4
64.3
88.8
94.0
82.3
60.7
80.6
62.6
68.5
64.9
67.8
+DIDA
T
89.2
55.9
89.6
49.0
8.4
58.9
66.1
64.4
88.5
94.6
80.6
59.4
80.2
69.2
66.7
68.4
68.6
Ablution for Informae Meds
Ablatis for Flned Tims baforeace
140
1925
1650
Inybet teleenro
60
53-
30-
52-
43
68
Eeplica Dowildnn Infermo
25
50
15
25
50
Figure 3: Demonstration of two modes of inference.
Duffeidee Time
Diffeiiie Time
Figure 4: The performance variation with the degraded level.
features. In the end, the extra diffusion encoder is ablated (rows 6 and 7). Although Gtime can perceive the semantic shift implicitly enabled by time embedding, it more effectivee introduce an extra diffusion encoder to compensate for the lost discriminative representation.
How DIDA Works. To further comprehend the working mechanism of DiDA, we design two modes for inference on degraded images with known diffusion time I, shown in Fig. 3. The first mode, called implicit denoising inference, the same what used in the training phase of DiDA. The network So takes degraded images and as inputs and generates segmentation results immediately. In contrast with this mode, the second mode segments the degraded images indirectly by predicting the noise through fa first for reconstruction and feeding them back to the network fe. which shares the same weight with fo but no diffusion encoder, obtain the final prediction. This mode is termed explicit denoising inference. We evaluate the performance difference between the two modes on different noise levels and construct two baselines for comparison. The strong baseline, called base(S), trains and inferences each model separately on intermediate domains with different noise levels. For the weak baseline, termed as base(W), we execute inference on these intermediate domains with fo. Fig. 4 (a) plots the performance curve throughout the diffusion forward process. The performance with implicit denoising inference slightly lower than the explicit mode, and the gap between them is tiny. Both modes beat the strong baseline using single network trained only once. It indicates that DiDA can perceive the semantic shift precisely and extract features in an implicit denoising manner during the training phase. Furthermore, unlike the explicit mode, the network fe learns features directly from the degraded images to bridge the domain gap and heighten the adapting ability. These two factors jointly contribute to improving overall performance on UDA segmentation.
Inference with Fixed Diffusion Time. Based on the above discussion, we further evaluate the performance for inference with different fixed diffusion times t. The results are summarized in Fig. 4 (b). Note that / = is equivalent to inference without the diffusion encoder, i.e., So. which is the same as the weak baseline defined above. With the change of fixed diffusion time, there are two common properties among these curves: (i) the performance decreases along with the forward diffusion process, and (ii) at each fixed level of the forward process, inference with the same fixed

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diffusion time can achieve the current best precision, which is in accordance with the intuition. We can deduce that the higher t is fixed in the inference procedure, the flatter the corresponding performance curve will be. Although the optimal performance degrades, this phenomenon reveals a desirable property: inference with a fixed diffusion time can enhance the network's ' robustness against input perturbations, thereby improving its anti-interference capability.
5
Conclusions
In this paper, we propose DiDA, degradation-baser bridging framework for domain adaptive semantic segmentation. By simulating intermediate domains through simple image degradations and formalizing them as diffusion process, DiDA effectively mitigates semantic shift and promotes domain-invariant feature learning. The framework is general and modular, supporting various degradation types and seamlessly integrating with diverse UDA methods and backbones. Extensive experiments demonstrate that DiDA consistently improves performance and achieves new state-of- the-art results on multiple standard UDA benchmarks.
Acknowledgements
This work partially supported by the National Key R&D Program of China (Grant No. 2024YFB3909902), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (CAS).
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A Implementation Details of DiDA
In this section, provide the pseudo algorithms explain implementation details of DiDA, shown in Alg, 1. Our method is designed as a plug-and-play module applicable to any trraining-based UDA framework, introducing no additional computational overhead during inference.
Algorithm Pseudo algorithms DiDA.
I: Inputs: Source Domain D, = (> y ;=1-
Ns Target Domain D, = (x Nr
2: Define:
Student Network fe. Teacher Network fe. Diffusion Encoder g Reconstruction Head h', Noise Schedule a, Diffusion Steps T, Momentum Coefficient |
3: Output: Student Network fo
4: for each batch of (x:² .. ) in D., D, do
!
5: # Source Domain:
6: Calculate L for fo by Eq. (1)
b. Supervised loss
7: # Target Domain:
8: Obtain pseudo-labels from fo by Eq. (3)
9:
Calculate L for fe by Eq. (4)
b Adaptation loss
10:
# Degradation-busee Intermediate Domain Construction:
11:
Sample ~ Uniform(1,1 T)
12:
Obtain degraded images 2h,0. x²,, by Eq. (6)
13:
# Semantic Shift Compensation:
14:
Calculate _ for fo ho o (g + g') by Eq. (10) Calculate L for So h' (g + 9') by Eq. (7)
Degraded image consistency loss
15:
Reconstruction loss
16:
# Training:
17:
Gradient backward L LT + AD ARC
Update student model
18:
# EMA Update:
19:
+ 36 + (1 3)
Update teacher model
20: end for
B Details in Expansion Versions
In this section, we describe the details of our implementation for expansion versions of DiDA, in which we replace the degradation operation in the diffusion forward process with blur and mask.
Image Blur. Given the Gaussian kernels (G ), the forward process can be simply defined as:
It = G2 It-1 = G3 ...Gi * 2g G2 xo.
(11)
where represents the convolution operator to apply the Gaussian blur operation on the image. Then, the model fe is trained for deblurring to invert this blurred diffusion process.
Following the setting of cold diffusion [2]. we define T Gaussian kemels: GI...,, GT to execute gradual blurring. For instance, we set = 100 with a Gaussian kernel of 31 31, and the standard deviation of the Gaussian kernel grows exponentially with time at the rate of 0.02.
Image Mask. To implement the incremental mask operation on the image with the diffusion time steps, we define this process with cowmask [27]. With the schedule ā, as threshold, we can generate the cowmask M, and obtain xe by element- wise multiplication of the mask and image:
It = M To.
(12)
and the inpainting model trained to restore the image. The procedure for generating a masked image with cowmask and is provided in Algorithm 2, and we wet the std d = 6.
To extend our method with the above-defined forward process, we only need to replace the degraded image sampling operation and modify the reconstruction loss to predict the clean image directly:
R lo fo (x,, - 2o11
(13)
where X, is a I-dependent loss weight, defined as fixed value computed from the noise schedule {a, [T_ [73]. introduced to balance the contribution of different degradation levels.

---

Source
Targe
(a) Blur
Source
Targer
(b) Mask
Figure 5: The examples that extend DiDA to any other forward diffusion process defined by arbitrary image degradation operations. (a) shows image blurred process and (b) shows image masked process.
Algorithm 2 CowMask generation algorithm with the threshold E [0, 1] as the ratio.
Require: original image o threshold т. std
Ensure: masked image ..
1: sample Gaussian noise e ~~ (O,I)
2: filter noise E, moussian_fiitee 2d(e, 8)
3: compute mean m mean(Es)
4: compute std dev std_ std_deul( dev((c)))
5: compute noise threshold p
/2er / -1 (2T 1)s
6: threshold filtered noise M = Es <
7: mask image x M5 = 0 o M
8: return -.
C Architecture of Time Embedding Module
In this section, we illustrate the details of the time embedding module introduced in different backbones. As shown Fig. 6, we condition all blocks of models on t, whether Transformer- based architecture or CNN-based architecture. The diffusion time is first encoded by Transformer sinusoidal position embedding [84] and projected to time embedding through 2 layers MLP. Then, the time embedding is encoded by M..P; and M MLP; to obtain shift and bias and ensure the same channel dimension as the corresponding feature. In the end, each block's 's internal feature Sa modulated through multiplication and addition operations at the channel dimension.
25

---

block
block,
FFN
t
CONV:
Positional Encoding
ft
Positional Encoding
fi
MLP!
MLP
2xMLP
2&MLP
MLP
MLP,
Time Embedding
Time Embedding
CONY
MLP;
CONY
block;
block:
block
block
(a) Transformer Block
(b) ResNet Block
Figure Architecture of time embedding module different backbones.
Table 5: UDA segmentation performance on ACDC, where the improvement by DiDA is marked as bold. The results are acquired with CNN-based model (C) in the first group Transformer- based model (T) the second group. denotes the reproduced result.
Sidemalk
Buildng
Road
Wall
Fence
Pole
Person
Riles
180
Train
Molor
Method
ADVENT [85]
72.9
14.3
40.5
166
21.2
93
17.4
21.2
63.8
23.8
18.3
52.6
19.5
09.5
36.2
34.5
46.2
26.9
36.1
32.7
DANNel [89]
C
84.3
54.2
77.6
38.0
30.0
18.9
41.6
35.2
71.3
39.4
50.6
48.7
29.2
76.2
41.6
43.0
58.6
32.6
43.9
50.0
DXPormer [38]
58.4
38.1
309
39.0
748
13.6
82Y
549
82.4
44.1
50.7
+DIDA
T
68.5
52.5
R2.5
43.4
42.7
59.8
60.3
53.4
75.7
80.2
63.7
99.3
30.4
87.3
68.6
76.1
79.5
41.9
36.6
59.1
LIRDA Doj
681
575
8K
36.7
36.3
74.
82.4
+DIDA
90.7
S0.1
68.7
79.1
73.0
91.5
56.3
MIC. [20]
T
50.1
65.0
67.7
55.5
43.3
60.6
63.
66.
75.
54.3
K5.0
69.3
47.4
88.6
50.7
89.5
88.8
55.4
99.1
693
+DIDA
D Additional Experiment Results
In this section, we evaluate the performance of our method on the CS. adaptation task, which involves large domain shifts caused by adverse weather conditions such as rain, snow, and nighttime. As shown in Tab. 5, DiDA consistently improves the performance of multiple baseline models, including DAFormer, HRDA, and MIC, with a gain ranging from 2.3% 3.7% mloU. These improvements are highlighted in bold.
We attribute DiDA's superior performance in this scenario the similarity between image degradation operations and real-world image corruptions caused by bad weather. Notably, the improvements are especially significant in classes that typically affected by weather degradation, such as traffic light, sign, and pole. The degradation-awarr training in DiDA (e.g., blur, noise, contrast reduction) helps the model learn more robust and generalizable features that align well with the characteristics of the ACDC dataset. In other words, DiDA implicitly narrows the domain gap by simulating weather-ing weather-induced artifacts during training.
E DiDA Efficiency Analysis
In this section, we analyze the computational overhead introduced by DiDA in terms of GPU memory usage, iteration time, and total training time. As summarized in Table 6, while DiDA brings consistent
26

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performance improvements across various UDA baselines, it also introduces moderate increases in computational cost.
Specifically, DiDA increases the GPU memory usage by approximately 60-70%, while is computa-
tionally efficient, with increase of 0.3-0.5s per iteration for each method. This overhead mainly stems from the additional modules introduced by DiDA, as illustrated in Algorithm 1. These include: (I) the diffusion-based degradation encoder g', used handle intermediate degraded domains; (2) the reconstruction head /1, which enforces semantic consistency via reconstruction and consistency losses (CD, ) (3) additional forward passes on degraded inputs for both source and target do- mains. Despite this increase in resource usage, DiDA's design is modular and parallelizable, and the degradation-bian based learning can be seamlessly integrated with existing teacher-student frame- works. We argue that the trade-off is justified, as the performance boosts brought by DiDA (up to +3.7% mloU) outweigh the moderate increase in training cost, especially in challenging real-world adaptation scenarios like ACDC.
To further enhance the efficiency of our method, we additionally provide a memory-friende variant, denoted as DiDA (ftim).. ). As shown in row 6 of Table 2, this version avoids the explicit use of the diffusion encoder, and instead implicitly models the t-specific semantic shift through a lightweight time embedding. Despite its simplified design, DiDA (fume) still achieves a comparable performance gain when compared the full version (row vs. row 7). By eliminating the diffusion encoder, DiDA (gime) significantly reduces GPU memory consumption saving up to 15 15-20% compared to the full parsion-while also slightly accelerating the training process. This makes it practical trade-off option for scenarios with limited hardware resources.
Furthermore, DiDA is only applied during the training phase. No architectural modifications additional computations are introduced at inference time, ensuring that the inference speed and resource consumption remain identical the baseline models.
Table 6: Computational resource requirements comparison
Methods
GPU Memory (Mme per iter (s)
Total Time (h)
DAFormer [38]
9,807
1.32
14.5 (40K iters)
+DiDA (gtsme)
12754
1.59
17.8 (40K iters)
+DiDA
15856
1.64
18.5 (40K iters)
HRDA [39]
22325
2.11
23.5 (40K iters)
+DiDA (gtsme)
16727x2
2.58
28.7 (40K iters)
+DiDA
19337x2
2.64
29.4 (40K iters)
MICT40]
22370
2.60
28.9 (40K iters)
+DiDA (gtime)
16813 x2
3.03
33.7 (40K iters)
+DiDA
19343 x2
3.13
34.8 (40K iters)
Influence of Parameter Settings
In this section, we further study the influence of parameter settings introduced in DIDA, i.e., DIC loss weight ÅD. reconstruction loss weight An, and diffusion steps T. All experiments are conducted with DAFormer [38] on GTA
DIC Loss Weight Åp- We first study the weight of the DIC loss Ap in Tab. 7. The weight for the DIC loss is sensitive to the UDA performance. Reducing the weight progressively diminishes performance until no DIC is used. A larger weight also results in decreased performance. If the weight is too large, it can lead significant decline performance due excessive disturbance.
such Ap
Reconstruction Loss Weight AR. The weight of the reconstruction loss is also studied in a similar way (see Tab. 8). Based on the analysis above, we can draw a similar conclusion, with the difference being that this loss item has a slightly diminished impact compared to the previous one. The results demonstrate that values ranging from to 10 consistently yield good UDA performance, providing reasonably wide range for selecting robust hyperparameters.
Diffusion Steps T. We also study the value selection of diffusion steps T (see Tab. 9). Notice that too large a value, like = 1000, does not result in optimal performance, which is the default setting for DDPM [37] to facilitate high-quality generation. In the UDA setting, the number of iterations
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and batch size is far less than the requirement to train the generative model. Therefore, we make adjustments reduce the diffusion steps T, ensuring they are more suitable for this task. In our experiments, value around 100 can achieve consistently good performance.
Table 7: Parameter study of loss weight Ap.
Table Parameter study of loss weight An-
Weight Ap 0 0.1 0.25 0.5 1 5
Weight Xn 0 1
2.5 5 10
50
mlou 67.9 69.6 70.1 70.3
69.8 64.1
mlou 69.5 70.0 70.0
70.3
70.2 67.7
Table 9: Parameter study of diffusion steps T.
Table 10: Different strategies of time schedule.
Diffussio Steps T
10 50
100
200 1000
Time Schedule linear cosine sigmoid mlou
mlou
69.3
70.2
70.3 70.0 70.1
69.8
70.2 70.3
Table 11: Results on GTA. built with ex- tended versions.
Table 12: Results on SYN. built with ex-
tended versions.
Method
base
B
M
Method
base
B
M
DAFormer [38]
68.3
70.0
69.8
DAFormer [38]
60.9
63.1
62.6
HRDA [39]
73.8
75.3
74.9
HRDA [39]
65.8
68.0
67.7
MIC [40]
75.9
76.7
76.6
MIC [40]
67.3
68.7
68.2
G Influence of Time Schedule
We study different choices of time schedules for 3r, namely, linear [37], cosine [65], and sigmoid [44], with the same setting as Sec. The results are summarized in Tab. 10. Although selecting the appropriate schedule is significant for generating high-quality images in the diffusion model, it is robust DiDA, and we can achieve consistently good performance among these strategies.
H Degradation-Based Domain Bridging
To further investigate the feasibility of our proposed motivation and provide deeper insight into the domain bridging mechanism, conduct quantitative analysis of the distribution discrepancy between domains. Specifically, we train models independently within different distribution spaces and evaluate their performance in terms of mloU under both fully supervised and UDA settings, as shown in Fig. 7(a).
To better assess the degree of domain adaptation, we further report the relative performance in Fig. 7(b), defined as the ratio of UDA mloU the fully supervised counterpart each degradation level. As the level of image degradation increases, observe gradual improvement in the relative performance, suggesting that the adaptation capability is enhanced. This indicates that domain-share information is better preserved in the intermediate domains constructed via degradation.
However, image degradation inevitably destroys fine- mne-grained visual details, which may include essential semantic cues. As result, the semantic shift problem arises, where the corrupted features impair the discriminative power of the model and hinder further performance gains in UDA.
Our proposed DiDA framework addresses this issue by explicitly disentangling and compensating for the semantic shift through a diffusion-basee encoder and reconstruction loss. This design enables the network to retain domain-invariant invariant representations while mitigating the adverse effects of degradation, thereby achieving consistently better adaptation performance.
I More Results with Extended Versions
We further conduct more experiments about Blur (B) and Mask (M) built with DAFormer [38] and HRDA [39] and evaluate the performance on two benchmarks as shown in Tab. 11 and Tab. 12. These extended versions show consistent gains beyond all baselines, which further demonstrates the generality and expansibility of our framework.
28

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oifore
034
(a) Absolute Performance
(b) Relative Performance
Figure 7: (a) The absolute performance of fully supervised learning and UDA settings (source-only, DAFormer [38] and DiDA) when the proportion of noise increases. Note that train the models separately within different intermediate domains for the first three methods, while DiDA is trained only once and tested within different distributions. (b) Compared to oracle, the relative performance of UDA settings gradually improves, which means that the model's adaptability strengthened in these intermediate domains.
Images
DAFormer
DAFormer+Diin
MIC
MIC-DIDA
Ground Truth
Road
SMewalk
Buiding
Wall
Fole
Tradft light
Bratl:c dis
Vegetadion
Sky
Paesen
Biake
Car
Irusk
Ban
Train
Matasscle
Bicyde
Figure 8: Qualitative comparison built with DAFormer and MIC. The dotted boxes mark regions improved by DiDA.
J Qualitative Results
In Fig. 8, we illustrate the qualitative comparisons of our DiDA against DAFormer and MIC. The previous methods fail to identify some classes the target domain when their visual textures significantly different from the source domain and confuse them with other visually similar classes (e.g., sidewalk and fence and building, terrain and vegetation). In this case, DiDA makes the model recognize semantic categories more dependent on context information, resulting in improved cross-domain performance.
K
Visualization of Reconstruction Results
We visualize examples of reconstruction results in Fig. 9 with = 25, 50, 75 in the time embedding module. The model can yield the best reconstruction results for degraded images with the corre- sponding t value. A fixed value of leads to inadequate denoising for higher levels of degradation
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Degraded
50
Degraded By 25
30
75
Denoise
Deblur time
tine
25
50
50
75
75
(x) Examples Image Denoising
(b) Exampless Image Deblurting
Figure 9: The examples of image reconstruction results with fixed diffusion time 25, 50, 75 as input in the time embedding module, where the image is degraded by 25, 50, and 75, respectively.
Degraded by
25
75
100
time
25
75
100
Figure 10: Examples of image denoising, horizontal coordinate represents the level of image degrada- tion, and vertical coordinate indicates the f used in the inference.
and excessive denoising for lower levels of degradation, meaning that degradation information is accurately encoded in the time embedding. This further alleviates the semantic shift phenomenon and facilitates adaptive learning from the disturbed image.
L More Examples for Image Reconstruction
In this section, give further details, analysis, and examples of image reconstruction. For different versions of the implementation of DiDA, we execute the inference with different diffusion times t on varying levels of image degradation for qualitative analysis.
Image Denoising This is the default setting of DiDA, the noise e' is predicted by network fo firstly and used to reconstruct the image as:
[z: -1- aee)
(14)
The examples are shown in Fig. 10. The noised images can be restored most perfectly when the diffusion time matches the level of degradation. We can observe that the higher value has more powerful anti-noise ability, which is consistent with our experimental results previously stated.
30

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Degraded 0
25
30
100
time
25
50
100
Figure 11: Examples of image deblurring, horizontal coordinate represents the level of image degradation, and vertical coordinate indicates the t used the inference.
Degraded by
25
50
75
100
time
25
50
75
100
Figure 12: Examples of image inpainting, horizontal coordinate represents the level of image degradation, and vertical coordinate indicates the / used the inference.
31

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Image Deblurring, In the following two settings, the reconstructed image is directly predicted by fo. Fig. 11 shows examples of image deblurring. We can draw similar conclusion that the matched achieves the best performance as above. The higher i results in excessive deblurring operation while the structure and edge information of the image are preserved, leading to more robust performance.
Image Inpainting, Fig. 12 shows examples of image inpainting. Unlike the former, when the variable t changes the inference, the reconstruction quality for the masked image does not vary significantly, Since the mask operation is operated in a global view to control the ratio while the previous degrading operation is executed locally, is difficult for the network to sense the level of degradation in this case. Therefore, DiDA achieves slighter performance gains with the implementation of mask operation.
M More Discussion with Domain Bridging
Directly transferring knowledge from the source domain to the target domain can be challenging due to significant discrepancies and pixel-wise gaps between the domains. To address this issue, some works propose gradually transferring knowledge by building a bridge between the source and target domains. This is achieved by constructing intermediate domains at the image level [64, 92, 94], feature level [22, 23, 58], or output level [41, 98]. One line of work utilizes style transfer techniques [16, 19, 30] to transfer the style of source data to target data, effectively creating intermediate domains. Another approach leverages data mix augmentation techniques [82, 102, 7]. such as CutMix [96] and Mixup [97]. to construct various intermediate domains. While these methods can effectively reduce the domain gap and facilitate the adapting ability of models, they have certain limitations. Style transfer-based methods are often dataset-specific and may generate unexpected artifacts, while data mix strategies can disrupt the contextual distribution of images and require intricate designs. Both approaches lack generalization ability and face the semantic shift problem, where the intermediate domains may not preserve the semantic information of the original domains. These limitations restrict their applicability in different scenarios. In contrast, our work aims to explore a universal and concise domain bridging strategy that can be casily integrated into existing UDA frameworks while explicitly compensating for the discriminative representations. By constructing intermediate domains through diffusion forward process, we propose dataset-agnostie approach that alleviates the semantic shift problem and enhances the generalization ability of the model.
N
Limitation and Societal Impact
Our work presents general and modular approach for domain adaptive semantic segmentation. While our method demonstrates consistent improvements across multiple UDA baselines and datasets, it still presents few limitations. First, DiDA introduces moderate computational overhead during training due to additional modules such as the degradation encoder and reconstruction losses. Although we provide eeemory-friendie variant, further optimization is needed for extremely essuucce-consrring environments. Second, the choice and combination of degradation operations are currently heuris- tie and manually designed; automating or learning this selection process could further enhance performance and generality.
This work focuses on domain adaptive semantic segmentation, a key area in computer vision with broad applicability in domains such as autonomous driving, medical imaging, and remote sensing. At present, we are not aware of any direct negative societal impacts associated with the proposed method.
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Reviews:
[{"review_id": "Qp2vZWL04T", "reviewer": "Reviewer_Wf8D", "summary": "The paper presents the DiDA Framework, an unsupervised domain adaptation approach for semantic segmentation. The insight idea of this paper is from the diffusion model, where the authors gradually add noise into the source and target domains (degradation steps) in order to remove domain-invariant features as domain differences gradually diminish. After that, the Semantic Shift Compensation module is proposed to mitigate the semantic shift problem during intermediate domain reconstruction. Overall, the paper's idea sounds interesting.", "questions": "Please refer to my weakness section. \n\nEqn. (7) has an extra parenthesis.", "limitations": "Yes. The authors discuss the limitations in the appendix.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper is well-motivated with interesting theoretical insight.\n- The paper presentation is good. \n- The idea of the proposed method is easy to catch via Figures 1 and 2.\n- The experiments are solid and comprehensive. \n\nWeaknesses\nThe method introduces four loss functions and a quite heavy end-to-end framework, which raises concerns about the stability of the framework. \n- The demonstration of the intermediate domain is still vague, and hard to understand the flow of that module. It would be better it the authors further clarify this one.\n- The motivation of Semantic Shift Compensation should be further explained.\n- Did the authors discuss the trade-off between using larger $t$ and semantic information?. It means a larger timestep $t$ would reduce the domain gap; however, the semantic information is harder to learn. How to balance two factors? In addition, the timestamp is large, and the image becomes totally noisy. How can the model learn semantic segmentation from a totally noisy image? \n- The inference step is not clear. How are compensatory features used in the inference step? According to Figure 2, it is fed directly into the segmentation head. Is it concatenated with any features or so?\n- The authors mentioned ACDC in the experimental section, but I do not see the experiments of ACDC in the main paper. Please carry the experimental results of this benchmark into the main paper.", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns. Although the presentation of some parts in the paper could be further improved, the paper's idea sounds interesting, and the experimental results have illustrated the effectiveness of the proposed approach. Therefore, I would like to give a borderline accept.", "rebuttal": "Thank you for taking the time to provide detailed and constructive comments on our work. We greatly appreciate your positive feedback regarding the **interesting and intuitive idea**, **well-motivated theoretical insights**, **good presentation**, and **solid and comprehensive experiments**.\n\nWe provide a detailed point-by-point response to your comments.\n\n------\n\n**Q1:** Stability of The Framework\n\n**A1:**\n\nThank you for highlighting this important concern. While DiDA introduces additional components to enhance domain adaptation, it has been **carefully designed to ensure training stability, modularity, and practical efficiency**. We address the concern from four perspectives:\n\n1. **Controlled Complexity of Loss Functions:**  \n   DiDA introduces only two additional loss terms beyond standard self-training:  \n   - Degraded Image Consistency Loss ($L^D$), which regularizes predictions under degradation.  \n   - Reconstruction Loss ($L^R$), which supervises semantic recovery.  \n\n\n   Both are explicitly weighted in Eq. (10), and we conduct comprehensive ablation and sensitivity analysis in Appendix F     (Tables 7 and 8), showing that DiDA maintains stable performance across a wide range of hyperparameter values. These losses are complementary and do not introduce optimization instability.\n\n\n2. **Modular and Loosely Coupled Components:**  \n   As shown in Table 2, each module in DiDA can be independently removed or replaced without causing performance collapse. This modularity ensures robustness, and the architecture can be easily adapted or simplified depending on resource constraints.\n\n3. **Efficiency and Lightweight Variant:**  \n   We provide a memory-efficient variant, DiDA ($g\\_{time}$), which removes the diffusion encoder and retains only the time embedding. Despite its simpler design, it achieves comparable improvements (see Appendix E, Table 6), reducing GPU memory usage by up to 20%.\n\n4. **Inference-time Simplicity:**  \n   Crucially, DiDA is used only during training. At inference, all auxiliary modules are disabled, and the model reverts to a standard segmentation network. This design ensures no additional latency, memory, or model complexity during deployment.\n\n5. **Empirical Generalization Across Setups:**  \n   DiDA demonstrates consistent and significant improvements across diverse architectures (CNNs and Transformers), datasets (GTA→CS, SYN→CS, CS→ACDC), and UDA baselines (DAFormer, HRDA, MIC), as shown in Tables 1, 3, and 4. This cross-domain robustness supports the stability of our design.\n\nWhile DiDA introduces two additional loss terms and auxiliary modules during training, the framework remains stable, modular, and efficient. The consistent performance gains across benchmarks validate its practical reliability.\n\n------\n\n**Q2:** Clarification of Intermediate Domain Construction\n\n**A2:**\n\nThank you for pointing out the need for further clarification regarding intermediate domain construction.\n\nThe Degradation-based Intermediate Domain Construction module is based on the insight that **gradually degraded images create a continuum of distributions between source and target domains**. This strategy exploits the progressive removal of domain-specific features (e.g., texture) while retaining domain-invariant semantics (e.g., shape), thereby narrowing the domain gap in a controllable manner.\n\nTo clarify the mechanism:\n\n- Figure 1  conceptually illustrates the forward diffusion process, where domain-specific information fades earlier (e.g., texture), and semantic shape information remains longer, under the guarantee of the theoretical insight (Section 3.2).\n- Section 3.3 formally defines these intermediate domains as samples from the forward process $x_t = \\sqrt{\\bar{\\alpha}_t} x_0 + \\sqrt{1 - \\bar{\\alpha}_t} \\epsilon$, where $t$ controls degradation level.\n- Figure 4 shows performance trends across different degradation levels, empirically validating the effectiveness of intermediate domains.\n- Appendix H (Figure 7) presents a quantitative analysis: as degradation increases, the relative adaptation performance improves, suggesting that intermediate domains provide better alignment priors.\n- Appendix L shows image-level reconstructions across noise steps (Figures 9–12), visually confirming that core semantics are preserved even under significant degradation.\n\nWe will revise the main paper to better explain the flow of degraded domain construction and directly reference these supporting sections.\n\n------\n\n**Q3:** Motivation for Semantic Shift Compensation\n\n**A3:**\n\n\nThank you for raising this fundamental question. The Semantic Shift Compensation module addresses a critical limitation in degradation-driven adaptation: **While degradation helps eliminate domain-specific cues, it may also corrupt domain-invariant semantics—especially under strong noise—leading to *semantic shift* and degraded predictions.**\n\nTo mitigate this, we introduce a diffusion encoder ($g'$) and reconstruction head ($h'$), both conditioned on time-step embeddings. Together, they:\n- Perceive the extent of degradation (via time embedding),\n- Model the semantic shift caused by degradation,\n- Recover lost semantic structure (supervised by reconstruction loss $L^R$),\n- Regularize prediction consistency under noise (via $L^D$).\n\nThis design allows the model to preserve meaningful features even under strong degradation, as shown by:\n\n- Appendix K (Figures 9–12): Visualizations of reconstructions under different degradation levels.\n- Appendix M: Comparison with existing domain bridging methods shows our approach performs better in preserving semantics while reducing domain gap.\n- Table 2 (Ablation): Removing this module leads to a noticeable drop in performance.\n\nWe will enhance the main text to better illustrate this motivation and its benefits.\n\n------\n\n**Q4:** Trade-off between Larger Timestep and Semantic Information\n\n**A4:**\n\nThis is an excellent observation. Indeed, there is an intrinsic trade-off:\n\n- Small $t$ values: low degradation → retain semantics but maintain domain gap.\n- Large $t$ values: high degradation → reduce domain gap but risk semantic loss.\n\n**Rather than manually tuning this trade-off**, DiDA leverages random sampling of $t$ during training (uniformly from $1$ to $T$), similar to standard diffusion training. More importantly:\n\n- The time embedding module enables the model to perceive degradation level.\n- The diffusion encoder ($g'$) adapts feature representations accordingly.\n- The combined effect allows the model to implicitly learn how to balance domain invariance and semantic fidelity.\n\nEmpirical evidence:\n\n- Figure 4(b) shows performance under fixed $t$: when $t$ is too large, semantic loss dominates, but the model performs best when $t$ matches degradation.\n- Appendix H (Figure 7) shows relative performance gradually improves, which means that the model’s adaptability is strengthened across different $t$.\n- Appendix K (Figure 10) confirms that the reconstruction is optimal when $t$ aligns with degradation.\n\nThis design ensures that the model learns to adapt across a spectrum of domain proximity and semantic fidelity, without requiring manual balancing.\n\n------\n\n **Q5:** Inference Process and Use of Compensatory Features\n\n**A5:**\n\nThank you for pointing this out. We are happy to clarify the inference procedure and the usage of the diffusion encoder ($g'$) and reconstruction head ($h'$).\n\n- **Inference:**\n  - During inference, only the backbone segmentation network $f_\\theta = h \\circ g$ is used.\n  - The diffusion-specific components—$g'$ and $h'$—are entirely removed at inference time.\n  - This means no additional computation, parameters, or architecture changes are introduced in deployment, ensuring full compatibility with existing segmentation pipelines.\n\n\n- **Training:**\n  - The diffusion encoder $g'$ is used to model the semantic shift introduced by degradation by conditioning on the time step $t$. Its output is **added** to the main encoder’s output ($g$) at multiple feature levels via residual connections.\n  - This residual fusion strategy avoids architectural disruption, helps preserve the original semantics, and makes the integration of $g'$ stable and lightweight.\n  - This fused feature is then passed to:\n    - the segmentation head $h$ for calculating the Degraded Image Consistency loss ($L^D$).\n    - the reconstruction head $h'$ for the Reconstruction loss ($L^R$).\n\n   This design ensures that:\n  - The original encoder $g$ remains intact and continues to learn normally.\n  - The auxiliary encoder $g'$ enhances the representation by compensating for semantic degradation in a plug-and-play fashion.\n  - The training remains stable due to learnable residual modulation rather than full replacement or concatenation, which could otherwise destabilize feature distribution.\n\n\nWe will revise the main paper and Figure 2 to explicitly highlight this residual fusion mechanism and clarify that no concatenation or architectural modification occurs during inference.\n\n------\n\n**Q6:** Missing ACDC Results\n\n**A6:**\n\nThank you for noticing this. The ACDC benchmark is indeed important as it evaluates domain shifts due to adverse weather.\n- The main results for CS→ACDC are summarized in Table 1 (mIoU scores).\n- Detailed per-class results are included in Appendix D, Table 5.\n\nWe agree that this benchmark deserves greater visibility. In the final version, we will move the key ACDC results and analysis into Section 4, ensuring clarity and completeness.\n\n------\n\n**Q7:** Typo\n\n**A7:**\n\nThank you for pointing out this formatting issue. We will correct the extra parenthesis in Equation (7) in the final version.\n\n------\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions :)"}, {"review_id": "ZCB7nRO2ge", "reviewer": "Reviewer_xKyR", "summary": "The author introduces a novel domain adaptation method, termed DiDA (Degradation-based Intermediate Domain Adaptation), for the problem of unsupervised domain adaptation in semantic segmentation. This approach constructs a continuum of intermediate domains between the source and target domains by applying progressive image degradation operations such as Gaussian noise injection, blurring, or masking. These operations enable the model to gradually learn domain-invariant features as the domain gap is incrementally reduced.\nTo address the issue of \"semantic shift\", DiDA incorporates a time-conditioned diffusion encoder. This module compensates for and restores the semantic information lost during degradation, thereby preserving discriminative representations throughout the adaptation process.Extensive experiments conducted on multiple datasets and backbone architectures demonstrate that DiDA consistently outperforms both traditional domain adaptation methods and other intermediate domain construction techniques. Ablation studies further confirm the essential role of each component within DiDA, and provide evidence that the proposed method enhances both the stability and generalization capability of the model.", "questions": "1. Compare the training time with other methods.\n2. Compare with recent state-of-the-art methods to demonstrate the effectiveness of the proposed method.\n3. Results when using DiDA with MIC.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The authors propose a rational and innovative approach to domain adaptation for semantic segmentation by constructing intermediate domains based on image degradation. This strategy progressively narrows the gap between the source and target domains during training.\n2. DiDA is designed as a plug-and-play module, allowing for seamless integration with various network architectures and existing unsupervised domain adaptation methods. Extensive experiments on multiple standard datasets and diverse architectures demonstrate that DiDA consistently outperforms both baseline methods and other advanced domain adaptation techniques.\n3. Detailed ablation studies are conducted to evaluate the contribution of each component within the system, providing a comprehensive understanding of the essential roles played by different modules in the overall framework.\n\nWeaknesses:\n1. Due to the integration of the diffusion model, the training time increases significantly.\n2. The proposed method has not been compared with several recently introduced state-of-the-art approaches.\n3. When combined with MIC and DTS, the proposed method achieves only a marginal improvement (an increase of 0.3 in Table 3), indicating limited effectiveness in these scenarios.", "was_revised": true, "final_justification": "The author has provided very detailed rebuttals and has addressed most of my concerns as well as those of other reviewers. Based on these reasons I would like to raise my evaluation score to 5.", "rebuttal": "Thank you for taking the time to share your comments and suggestions in the review assessment. We sincerely appreciate your positive feedback on our **rational and innovative approach**, **plug-and-play design**, **extensive experiments**, and **comprehensive ablation analysis**.\n\nWe provide a detailed point-by-point response to your comments.\n\n\n----\n**Q1:**  Increased training time due to the diffusion model\n\n**A1:**  \n\n\nWe fully acknowledge that incorporating a diffusion-based mechanism introduces additional training cost. However, we argue that this is a **reasonable and justified trade-off** for the following reasons:\n\n- As detailed in Appendix E (Table 6), the training time increases from 14.5h to 18.5h on DAFormer and from 28.9h to 34.8h on MIC. This represents a moderate increase (~27%), especially considering the consistent performance gains of up to +3.7 mIoU across multiple settings.\n\n- To reduce training cost, we propose a memory-efficient variant, DiDA($g_{time}$), which removes the diffusion encoder while preserving most of the performance (only 0.4 mIoU lower than full DiDA; see Table 2). This version reduces both memory consumption and training time significantly.\n\n- Importantly, DiDA incurs no additional inference cost, as the diffusion-related modules are used only during training. This ensures deployment efficiency is unaffected.\n\n- To further demonstrate DiDA’s efficiency, we compare its training cost and performance with other plug-in methods (e.g., FST, DTS) using the same DAFormer baseline and training schedule. Please refer to **A4**. Compared to FST and DTS, DiDA achieves better performance with lower training time and memory requirements, further validating its practicality. \n\nOverall, we believe the performance–efficiency trade-off is well-balanced, and DiDA remains scalable and practical for real-world applications.\n\n----\n\n**Q2:**  Lack of comparison with recent state-of-the-art methods\n\n**A2:**   \n\nWe appreciate your suggestion. In Tables 3 and 4, we already compare DiDA against recent state-of-the-art UDA methods, including:\n\n- ADFormer and CoPT, both of which are ECCV 2024 methods.\n- Other recent methods such as MICDrop (also ECCV 2024) are also relevant, but our method **already surpasses** these works on the benchmarks we evaluate.\n\nAdditionally, as DiDA is designed as a **plug-and-play module**, we focused on integrating it into widely-used baselines such as DAFormer, HRDA, and MIC. Notably, MIC remains one of the strongest open-source UDA methods available, and DiDA consistently improves its performance.\n\nWe also note that DiDA, as a plug-in module, can be easily integrated into any future methods, which we plan to explore in follow-up work.\nIn the final version, we will include comparisons with more recent methods, if they are released and relevant within the NeurIPS submission timeline.\n\n\n----\n\n\n**Q3:**   Limited improvement when combined with  DTS\n\n**A3:**  \n\nThank you for raising this point. There appears to be a misunderstanding here.\n \nWe would like to clarify that in Tables 1, 3, and 4, DiDA is **built directly on MIC**, not DTS. DTS is listed **for side-by-side comparison** as another plug-in method.\n\nImportantly, MIC is already a near-saturation UDA baseline, achieving 75.5 mIoU on GTA→CS. Our DiDA still manages to improve it to 76.8 mIoU (+1.3), which is non-trivial in this high-performance regime. As shown in prior works, gains >1 mIoU in such settings are considered meaningful and hard to achieve.\n\nTo avoid confusion, we will revise the description in the final version to make the experimental design and integration strategy clearer.\n\n----\n\n **Q4:** Training time comparison with other methods\n\n**A4:**\n\nAs already shown in Appendix E (Table 6), we provide a detailed comparison of training time, GPU memory usage, and iteration cost. \nTo offer further comparison, we provide a direct training cost comparison with other plug-in methods such as FST and DTS built on the same baseline (DAFormer) and training schedule (40K iterations):\n\n| Method            | GPU Memory (MB) | Time per Iteration (s) | Total Training Time (h) | mIoU (%) |\n| ----------------- | --------------- | ---------------------- | ----------------------- | -------- |\n| DAFormer          | 9,807           | 1.32                   | 14.5                    | 68.3     |\n| +FST*             | 16,271          | 1.96                   | 21.9                    | 69.3     |\n| +DTS*             | 23,711          | 2.85                   | 31.3                    | 69.7     |\n| +DiDA($g_{time}$) | 12,754          | 1.59                   | 17.8                    | 69.9     |\n| +DiDA             | 15,856          | 1.64                   | 18.5                    | 70.3 |\n\nThis table highlights that DiDA not only achieves **the best performance**, but also maintains **competitive training efficiency** and **lower memory usage** than DTS and FST. Especially, DiDA($g_{time}$) offers a compelling balance between cost and accuracy, making it **practical even under resource constraints**.\n\n\n----\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions :)"}, {"review_id": "z22BjIQIi5", "reviewer": "Reviewer_rcDJ", "summary": "- **What**: This paper proposes DiDA, an unsupervised domain bridging framework for unsupervised domain adaptation (UDA). DiDA creates continuous intermediate domains between the source and target domain through simple image degradation operations, encouraging the model to learn domain-invariant features.\n- **How**: The method integrates two key modules into a standard UDA framework. (i) Degradation-based Intermediate Domain Construction formalizes the degradation process as a diffusion forward process, which creates an overlapping area between the source and target domain distributions. (ii) The Semantic Shift Compensation module addresses the problem of information loss from degradation by using a diffusion encoder conditioned on the degradation level. This encoder compensates for lost semantic information and augments the main segmentation encoder's features.\n- **Results**: DiDA consistently improves the performance of various baselines (DAFormer, HRDA, MIC) by +0.8% to +3.7% mIoU, on standard UDA benchmarks. Ablation studies confirm the all of DiDA's core components, especially the Semantic Shift Compensation module and the additional loss terms, are essential for the performance improvement.", "questions": "1. **Ablating the Source of Performance Gain in Implicit Mode**: The performance gap between the implicit and explicit modes is small but consistent. Where does this advantage come from? Is it uniform across all semantic classes? Could you provide a per-class breakdown of the mIoU difference between the two modes?\nFor example, the implicit mode might be better at preserving the context for large, structural classes (e.g., road, building) but slightly worse for fine-grained, small objects (pole, traffic light) that are more easily lost in noise.\nSuch an analysis would clarify the trade-offs of the implicit approach and provide deeper insight into what kind of features it excels at learning directly from degraded inputs.", "limitations": "yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n1. **Novel Domain Bridging via Image Degradation**: This paper introduces a new UDA paradigm that leverages a diffusion-inspired process. By progressively degrading images, it creates a continuous bridge of intermediate domains that forces the model to learn robust, domain-invariant features.\n2. **Principled Semantic Shift Compensation**: Unlike naive data augmentation, DiDA explicitly addresses the semantic corruption caused by degradation. It uses a dedicated diffusion encoder and reconstruction objective to disentangle and compensate for feature loss, which extensive ablation studies prove is critical for achieving performance gains.\n3. **Plug-and-Play and Zero-cost at inference**: DiDA is implemented as a modular add-on that seamlessly integrates with various state-of-the-art UDA backbones (e.g., DAFormer, HRDA, MIC) without requiring architectural changes. The entire degradation-aware module is discarded during inference.\n4. **New state-of-the-art performance**: On UDA benchmarks like GTA$\\to$Cityscapes, DiDA consistently improves strong baselines by 0.8-to-3.7 mIoU and achieves new state-of-the-art results, demonstrating its effectiveness and robustness across different adaptation scenarios.\n\n### Weaknesses\n1. **Unverified Claim of Feature-Space Bridging**: The paper's main claim is that image degradation creates an \"intermediate domain\" that bridges the source and target feature distributions. However, the evidence provided is indirect, relying on final mIoU improvements. The paper lacks a direct, intrinsic  analysis to prove that the feature representations of source and target domain actually become more aligned by degradation. A standard t-SNE or UMAP visualization of the encoder's features would be necessary to substantiate this claim. Such a visualization should demonstrate that (i) after DiDA training, the initially separate feature clusters for original source and target iamges (t=0) become more intermingled, and (ii) for heavily degraded images (e.g., t > 50), the two clusters almost completely overlap. Without this direct evidence, the performance gain could be attributed to a powerful regularization effect rather than genuine domain bridging.\n\n*My confidence in the method's claimed mechanism would increase substantially if the authors (i) provided direct feature-space visualizations (e.g., t-SNE) to prove domain alignment.*", "was_revised": true, "final_justification": "This work has many strengths (see #Strengths), and the authors have thoroughly addressed all of my concerns. I believe it deserves a **accept**.", "rebuttal": "Thank you for taking the time to share your comments and suggestions in the review assessment. We appreciate your positive feedback on our   **novel Domain Bridging  idea**, **principled module design**,  **plug-and-play implementation**, **new state-of-the-art performance**.\n\nWe provide a detailed point-by-point response to your comments.\n\n-----\n\n**Q1:** Unverified Claim of Feature-Space Bridging\n\n\n**A1:**  Thank you for raising this insightful and important concern. The central hypothesis of DiDA is that progressive image degradation, inspired by the forward diffusion process, incrementally removes domain-specific features while preserving high-level, domain-invariant semantics. This process naturally constructs a sequence of intermediate domains that facilitate alignment between the source and target feature distributions.\n\n=====================\n\nFirst, we emphasize that our method is not only empirically motivated, but also **grounded in a theoretical framework**. Specifically, in Section 3.2, we provide a formal proposition (titled Attribute Loss and Time Step) that explains how different semantic attributes are progressively corrupted along the diffusion process. This proposition has two key implications:\n- Fine-grained, domain-specific attributes (e.g., texture) are lost earlier in the diffusion process.\n- Coarse-grained, domain-invariant attributes (e.g., shape) persist longer.\n\nThis supports our hypothesis that intermediate degradation levels (i.e., specific time steps) correspond to intermediate domain representations that preserve shared semantics while discarding domain-specific noise. As a result, samples from the source and target domains become more similar in feature space as degradation increases.\nMoreover, this theoretical insight justifies our core claim: image degradation acts as a transport mechanism that brings both domain distributions closer in latent space. It also motivates the diffusion encoder design, which compensates for semantic shift while preserving alignment.\n\n=====================\n\nTo empirically validate this theoretical foundation, we provide a set of **quantitative analyses** in Appendix H (Fig. 7):\n\n- In Fig. 7(a), we report model performance under both fully supervised and UDA settings across varying degradation levels. Models are trained independently in each distribution space, allowing us to isolate the effect of degradation on adaptation quality.\n- Fig. 7(b) shows the relative UDA performance (i.e., UDA mIoU compared to the supervised upper bound) at each degradation level. As degradation increases, we observe a consistent rise in relative performance, supporting the idea that intermediate domains constructed by degradation better preserve domain-invariant structures and enhance domain alignment.\n\n=====================\n\nIn addition, as illustrated in Section 4.3 and Fig. 4(b) of the main paper, we analyze **inference behavior** with fixed degradation levels. Two key findings emerge:\n\n- Performance degrades gracefully with increasing diffusion steps, due to stronger corruption—this is expected.\n- However, at each fixed degradation level, the model achieves its best performance when the inference step matches the training degradation level, and the resulting accuracy curves become flatter as degradation increases. \n\nThis suggests the model learns more robust, stable representations in deeper intermediate domains.\n\n=====================\n\nTo provide more direct evidence of feature-level alignment, we have also conducted t-SNE visualizations of the encoder outputs:\n- Before training, the source and target domains form clearly separated clusters.\n- After DiDA training, these clusters become significantly more overlapped at low degradation levels (e.g., t = 0), and at higher degradation (t > 50), the source and target features nearly coincide.\n\nThis confirms that degradation serves as an effective bridge in feature space, not just pixel space.\n**Due to the rebuttal format constraints**, we are unable to include the t-SNE plots here, but we will incorporate them into the final version of the paper.\n\n=====================\n\nTogether, these analyses—quantitative trends, behavioral patterns, and qualitative visualizations—strongly support that DiDA's gain stems not merely from regularization, but from a principled domain-bridging mechanism grounded in both theory and practice.\n\n---\n\n**Q2**: Class-wise Performance of Implicit Mode\n\n**A2:**\n\nThank you for raising this thoughtful question. To better understand the source of the performance difference between implicit and explicit inference modes, we conducted a per-class mIoU comparison using a fixed diffusion step of $t = 50$ during inference. The results are summarized as follows:\n\n| Method    | Road | Sidewalk | Building | Wall | Fence | Pole | Light | Sign | Veg  | Terrain | Sky  | Person | Rider | Car  | Truck | Bus  | Train | Motor | Bike | **mAcc** |\n| --------- | ---- | -------- | -------- | ---- | ----- | ---- | ----- | ---- | ---- | ------- | ---- | ------ | ----- | ---- | ----- | ---- | ----- | ----- | ---- | -------- |\n| base (w)  | 93.5 | 61.8     | 85.0     | 38.7 | 25.1  | 38.2 | 42.3  | 52.1 | 85.8 | 43.8    | 87.8 | 64.4   | 35.9  | 86.8 | 52.8  | 61.2 | 58.3  | 41.9  | 58.0 | 58.5     |\n| base (s)  | 95.3 | 63.0     | 88.7     | 44.9 | 35.9  | 41.9 | 45.2  | 56.9 | 89.0 | 45.2    | 93.3 | 68.3   | 39.6  | 90.2 | 65.0  | 71.6 | 59.5  | 48.0  | 61.2 | 63.3     |\n| mode (Im) | 95.8 | 74.4     | 88.2     | 55.1 | 36.5  | 48.2 | 52.7  | 59.9 | 88.6 | 48.4    | 90.2 | 70.6   | 43.2  | 90.7 | 70.8  | 77.3 | 53.3  | 52.9  | 64.4 | 66.4     |\n| mode (Ex) | 95.6 | 69.2     | 89.1     | 56.2 | 37.4  | 48.4 | 51.8  | 60.9 | 89.2 | 47.1    | 90.1 | 70.4   | 44.1  | 92.0 | 71.7  | 81.0 | 57.2  | 52.1  | 63.8 | 66.7     |\n\nWe observe that implicit and explicit modes achieve comparable performance across most categories, with only minor fluctuations. \n\nThese results support our hypothesis that **DiDA’s modeling is global and class-agnostic**. There is no consistent or significant per-class bias introduced by either inference strategy. As described in Lines 309-326 and Figure 3–4 of the main paper, the two inference modes are **functionally equivalent**, differing only in how the semantic information is recovered at inference:\n\n- The implicit mode uses a **single forward pass** through the degradation-aware network $\\bar{f}_\\theta$  to make predictions directly from noisy inputs and the known degradation level $t$.\n- The explicit mode performs a **two-stage process**: it first predicts the added noise using $\\hat{f}\\_\\theta$ reconstructs a clean image, and then passes it through the $f\\_\\theta$  for segmentation.\n\nDespite this structural difference, **both modes rely on the same underlying features and share weights**, and both are trained with the **same objective and data**. Therefore, the observed performance gap is not due to optimization, but rather reflects the natural trade-off between robustness and feature disentanglement.\nSpecifically:\n\n- The explicit mode benefits from **cleaner inputs** after denoising, which slightly improves accuracy, especially for fine-grained  classes.\n- The implicit mode, on the other hand, demonstrates robustness to degradation and performs a **more efficient, one-shot inference**, which aligns with the training paradigm and reflects better generalization to noisy domains.\n\nAdditionally, both modes significantly outperform the base(S) and base(W) baselines, despite using a single shared model trained only once. This indicates that DiDA is capable of learning domain-invariant representations and performing semantic recovery implicitly, without needing explicit denoising at inference.\n\nWe will include these detailed per-class results in the appendix of the final version to provide more insight into the trade-offs between implicit and explicit inference. Thank you again for the thoughtful suggestion.\n\n---\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions :)"}, {"review_id": "hbck5CHCec", "reviewer": "Reviewer_1mYM", "summary": "The main idea is to introduce a Diffusion-based loss part as a Self-supervised learning to improve the intra-domain representation learning for UDA segmentation.", "questions": "Please see the weakness.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "1. The first contribution is similar to the second one. Both of them is for adding the diffusion noise to diffusion reconstruction.\n\n2. The framework (especially Figure 2) is similar to another published paper called \"PiPa\"[a] with student and teacher. Could you have a detailed comparison?\n[a] pipa: pixel-and patch-wise self-supervised learning for domain adaptative semantic segmentation \n\n3. The denotations are quite ambigous. \nSome terms are different between texts and figures.\n- What is g_i or g_j in the figure? They are different from the encoder g?\n- What do you mean \"attributes\" in Section 3.2? \n\n4.  Section 3.2 is just to reclaim the diffusion? \n\n5. I think \"Degradation-based Intermediate Domain Construction\" is a diffusion forward process, which is not a contribution. \n\n6.Typos. \nFigure2 \"Techaer\" -> \"Teacher\"\n\"Reconsturction\" -> \"Reconstruction\"\n\n7. Performance \nSome published SoTA works are missing. https://paperswithcode.com/sota/domain-adaptation-on-gta5-to-cityscapes\n\n8. It would be great to also compare the cross-city setting like Cityscapes->Oxford RobotCity.", "was_revised": true, "final_justification": "The result is good. The two contributions are somehow incremental compared to the diffusion process.  \n\nDuring the rebuttal, the author did not explain their contribution clearly. \n\nThe first and the second contribution are quite over-claimed, which is an entire diffusion forward and backward process.  \n\nBut the author splits the diffusion process as two contributions.", "rebuttal": "Thank you for taking the time to share your comments and suggestions in the review assessment. \n\nWe provide a detailed point-by-point response to your comments.\n\n---\n**Q1:**   Contribution Clarity\n\n**A1:**\n\nThank you for your insightful question. We would like to clarify the distinction between our two contributions:\n\n- Our first contribution lies in proposing a **new perspective for domain bridging**, inspired by a theoretical insight that interprets image degradation as a mechanism for aligning domains. This conceptual contribution is **general and does not rely on diffusion models**, and it offers a novel way to understand how domain-invariant representations can emerge through degradation.\n- Our second contribution provides a **practical and effective instantiation** of this perspective using the **forward process of diffusion models**. This implementation enables the construction of continuous intermediate domains, which progressively increase the domain overlap and facilitate robust adaptation.\n\nIn summary, the first contribution introduces a new theoretical lens for domain bridging, while the second demonstrates how to operationalize this concept effectively. The two are complementary yet distinct in novelty and impact.\n\n---\n**Q2:**  Similarity to PiPa\n\n**A2:**\n\nWe appreciate your reference to PiPa. As you correctly observed, both our method and PiPa adopt a **student–teacher structure**, which is a widely-used paradigm in self-training–based UDA methods, including DAFormer, HRDA, and MIC. This self-training framework is a **general foundation**, not a unique characteristic of PiPa.\n\nHowever, our method DiDA totally differs from PiPa in **both motivation, methodology, and learning objectives:**\n\n- **Motivation**: DiDA is driven by a theoretical insight grounded in degradation-based domain bridging, which is not explored in PiPa.\n- **Methodology**: PiPa relies on pixel- and patch-wise contrastive learning, whereas DiDA constructs **intermediate domains** through **image degradation** and performs semantic shift compensation using a diffusion-based encoder.\n- **Learning Objectives**: PiPa aims to improve representation through contrastive objectives, while DiDA focuses on learning from domain-shared distributions arising from degradation.\n\nWe will add a detailed comparison in the revised version to clarify these differences and avoid potential confusion.\n\n---\n\n**Q3:**   Ambiguous Notations in Section 3.2\n\n**A3:**\n\nThank you for pointing out the notational ambiguity. We would like to clarify that:\n\n- The terms **$g\\_i$** and **$g\\_j$** appear **exclusively in Section 3.2**, which is dedicated to presenting our **theoretical insight**. These symbols represent modular attribute transformations used in the analysis of attribute granularity within the diffusion process. They are **not part of the actual method design**, nor do they appear in the method diagram (Figure 2). We will revise the text to make this distinction clearer.\n- Regarding the term **\"attributes\"**, as defined in Proposition 3.2, they refer to **visual features of varying granularity**, such as fine-grained textures or coarse-grained shapes. The proposition formally establishes that attributes with finer granularity are lost earlier in the diffusion process due to their higher sensitivity to noise. This analysis is theoretical in nature, and it provides a conceptual foundation for interpreting image degradation as a mechanism for domain bridging. \n\nWe appreciate this feedback and will revise Section 3.2 to better communicate the theoretical nature of this analysis and its role in motivating our design.\n\n---\n\n**Q4:**  Section 3.2 \n\n**A4:**\n\nWe understand the confusion and thank you for the opportunity to clarify.\n\nSection 3.2 is **not a restatement of standard diffusion theory**, but rather a presentation of **new insight** tailored to the **UDA task**. Specifically:\n\n- We reinterpret the **forward diffusion process** from the perspective of **attribute granularity**, showing how different types of features (e.g., texture vs. shape) vanish at different time steps. This leads to the observation that the overlapping region of degraded distributions can serve as a **domain-shared space**.\n- These insights are **not part of the original diffusion model design**, but are instead proposed as a **novel conceptual framework** for understanding how degradation can facilitate **domain-invariant representation learning** in UDA.\n- This serves as a **task-specific theoretical foundation** and motivates the design of our DiDA framework, particularly the idea of constructing intermediate domains via degradation.\n\n\nThis **task-specific reinterpretation** of diffusion in the context of domain adaptation is, to our knowledge, novel and not previously explored. We will revise the section to make this  reinterpretation more prominent.\n\n---\n\n**Q5:**  On the Contribution of Degradation-Based Intermediate Domain Construction\n\n**A5:**\n\nThank you for this question. While the mechanism is based on the forward diffusion process, the novelty lies not in the mechanism itself, but in its task-specific application to domain adaptation. That said, although the Degradation-based Intermediate Domain Construction module is indeed simple in implementation, it is not an isolated tweak, but rather a principled, task-specific strategy derived directly from the theoretical insight presented in Section 3.2. Specifically:\n\n- We propose degradation not just as a noise process, but as a principled strategy to bridge domains by constructing a continuum of intermediate domains.\n- The degradation levels are theoretically grounded (Section 3.2) and ensure that domain-specific features are gradually removed, while domain-invariant ones are preserved and compensated.\n- The mechanism is analytically motivated, generalizable across datasets, and modular enough to be applied as a plug-in to existing UDA frameworks.\n\nWe believe this component offers a novel and insightful perspective on domain bridging in UDA. Importantly, while the implementation is concise, this simplicity reflects the elegance and universality of the underlying idea, rather than a lack of innovation.\n\n\n\n\n---\n**Q6:**   Typos\n\n**A6:**\n\nThank you for pointing this out. We will carefully review and correct all typographical errors in the revised version of the manuscript.\n\n---\n**Q7:**   Missing SoTA Comparisons\n\n**A7:**\n\nWe appreciate your suggestion. In Tables 3 and 4, we already compare DiDA against recent state-of-the-art UDA methods, including:\n\n- ADFormer and CoPT, both of which are ECCV 2024 methods.\n- Other recent methods such as MICDrop (also ECCV 2024) are also relevant, but our method **already surpasses** these works on the benchmarks we evaluate.\n\nAdditionally, as DiDA is designed as a **plug-and-play module**, we focused on integrating it into widely-used baselines such as DAFormer, HRDA, and MIC. Notably, MIC remains one of the strongest open-source UDA methods available, and DiDA consistently improves its performance.\n\nWe also note that DiDA, as a plug-in module, can be easily integrated into any future methods, which we plan to explore in follow-up work.\nIn the final version, we will include comparisons with more recent methods, if they are released and relevant within the NeurIPS submission timeline.\n\n\n---\n**Q8:**  Lack of Cross-City Evaluation\n\n**A8:**\n\nThank you for this valuable suggestion.\n\nWe have extensively evaluated our method on multiple widely-used and challenging UDA benchmarks, including **synthetic-to-real** and **clear-to-adverse-weather** scenarios, which already demonstrate the robustness and generalizability of our approach.\n\nWhile **cross-city** settings are indeed meaningful, we note that **existing UDA methods have not been systematically evaluated** under this setting, making it difficult to establish fair and informative comparisons. Moreover, the Cityscapes dataset itself already includes data collected from multiple cities, and its train/val split **inherently introduces a cross-city domain gap**. As such, the standalone “cross-city” setting may be relatively naïve in the context of UDA.\n\nNonetheless, we agree that explicit cross-city adaptation is a promising direction. We are currently extending DiDA to this setting and plan to include such results in future work.\n\n\n\n---\n\nWe hope our response can resolve your concern. Please do not hesitate to let us know if you have further questions :)"}]

Author rebuttal:

Meta-review: This paper presents DiDA, an unsupervised domain adaptation (UDA) framework for semantic segmentation that leverages image degradation to reduce domain gaps. The method creates intermediate domains through progressive degradation (e.g., noise, blur) and uses a diffusion-based encoder to preserve discriminative features while compensating for semantic shifts.

Strengths

* Demonstrates consistent improvements over state-of-the-art UDA baselines across GTA→Cityscapes, SYNTHIA→Cityscapes, and cross-city benchmarks (e.g., Cityscapes→Oxford RobotCar).
* Integrates smoothly with existing approaches (e.g., DAFormer, HRDA) without inference overhead.
* Offers an innovative reinterpretation of diffusion dynamics for domain alignment, supported by thorough ablations and new cross-city evaluations.

Weaknesses & Resolutions

* *Theoretical originality*: Initially questioned, but partially addressed by clarifying the adaptation of diffusion theory to UDA. Core contribution remains application-driven.
* *Empirical validation*: Strengthened through additional experiments, t-SNE results confirmed improved feature alignment, and a lightweight variant reduced memory usage by 20% with negligible performance loss.
* *Clarity & stability concerns*: Largely resolved in the rebuttal.


While some lingering concerns remain (e.g., theoretical nuances noted by Reviewer 1mYM), reviewers generally acknowledged the framework’s robust empirical results and practical contribution. Authors are encouraged to incorporate the rebuttal into the final revision and address the lingering concerns.


DiDA provides a practical, theoretically informed approach to domain adaptation, validated across diverse settings. Although its theoretical foundation builds on existing diffusion principles, the novel UDA-specific application and consistent performance gains strongly support its acceptance.
Decision: Accept (poster)