Title: Towards Unsupervised Domain Bridging via Image Degradation in Semantic Segmentation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Semantic segmentation

unsupervised domain adaptation
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.


Peer reviews:
comments: 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.

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.

2. The framework (especially Figure 2) is similar to another published paper called "PiPa"[a] with student and teacher. Could you have a detailed comparison?
[a] pipa: pixel-and patch-wise self-supervised learning for domain adaptative semantic segmentation 

3. The denotations are quite ambigous. 
Some terms are different between texts and figures.
- What is g_i or g_j in the figure? They are different from the encoder g?
- What do you mean "attributes" in Section 3.2? 

4.  Section 3.2 is just to reclaim the diffusion? 

5. I think "Degradation-based Intermediate Domain Construction" is a diffusion forward process, which is not a contribution. 

6.Typos. 
Figure2 "Techaer" -> "Teacher"
"Reconsturction" -> "Reconstruction"

7. Performance 
Some published SoTA works are missing. https://paperswithcode.com/sota/domain-adaptation-on-gta5-to-cityscapes

8. It would be great to also compare the cross-city setting like Cityscapes->Oxford RobotCity.

QUESTIONS:
Please see the weakness.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- **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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.*\n\nQUESTIONS:\n1. **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.\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease refer to my weakness section. \n\nEqn. (7) has an extra parenthesis.\n\nLIMITATIONS:\nYes. The authors discuss the limitations in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}