Organizations: Open Distributed Systems, Technical University of Berlin · Department of Computer Science, Aalborg University, Denmark · Fraunhofer Institute for Open Communication Systems, Berlin, Germany
Adverse weather conditions such as fog, rain, snow, and low illumination introduce structured visibility degradations that severely impair object detectors trained on clear-weather data. When source data are inaccessible due to privacy or trans- mission constraints, source-free domain adaptation (SFDA) becomes necessary. However, existing SFDA methods based on pairwise contrastive learning struggle to capture high-order semantics and often suffer from agreement collapse under severe corruption. We propose REUNION, a hypergraph-guided SFDA framework for object detection in adverse weather conditions. REUNION constructs a contrastive hypergraph over target-domain object proposals, encoding high-order relations through intra-image context, weather-aware grouping, prototype-based semantic anchors, and uncertainty-aware connections between reliable and low-confidence instances. To effectively exploit these structured relations, we introduce a group-wise Hyper-InfoNCE objective that optimizes representations at the hyperedge level, enabling semantic information to propagate from confident proposals to corrupted or low-contrast instances. Experiments with diverse benchmarks demonstrate that REUNION effectively mitigates domain shifts and achieves state-of-the-art performance in SFDA settings.
Figures & tables
Figure 1: In SFDA, source data is inaccessible, while target images suffer from structured degradation (e.g., fog, rain, night), leading to detection collapse. Existing pairwise contrastive methods fail in such noisy settings. REUNION builds a contrastive hypergraph over object proposals using multi-relation hyperedges (e.g., intra-image context, and cross-condition views) to propagate semantics from confident to low-contrast instances.
Type
Method
D-Rainy
D-Snowy
D-Foggy
D-Sandstorm
N-Clear
N-Rainy
N-Foggy
mAP
Lower-Bound
Source-Only
15.9
16.1
20.8
3.0
9.2
6.4
5.5
11.0
Higher-Bound
Oracle
41.9
50.9
48.1
45.7
39.7
35.5
37.1
42.7
UDA
MIC Hoyer et al. (2023)
21.0
23.2
29.9
26.2
17.3
10.9
13.8
20.3
MRT Zhao et al. (2023)
18.4
25.2
29.6
18.3
17.3
7.0
14.6
18.6
2PC Deng et al. (2021)
37.4
31.3
42.5
17.0
27.9
28.6
22.1
29.5
AT Li et al. (2022)
32.1
26.4
41.7
35.5
27.6
27.4
19.7
30.1
Table 1: Performance comparison on ResNet50 (in AP) of our method with other approaches for UDA/SFDA from Cityscapes to Cityscapes-Adverse.
Type
Method
AP Car
S
Source Only
19.5
UDA
DAF Chen et al. (2018)
22.4
PT Chen et al. (2022)
22.7
MIC Hoyer et al. (2023)
27.5
AT Li et al. (2022)
28.4
2PC Deng et al. (2021)
31.2
Table 2: Sim2Real Adaptation from Sim10K → Cityscapes-Adverse.
Figure 2: Qualitative results for the Cityscapes-Adverse dataset are displayed from left to right, showcasing the raw image, followed by results from DRU Khanh et al. (2024) , IRG VS et al. (2023) , SF-UT Hao et al. (2024) , and the proposed, with the ground truth presented on the far right.
Type
Method
AP S
AP M
AP L
SFDA
LPLD Yoon et al. (2024)
5.2
14.3
19.4
MT Tarvainen, Valpola (2017)
5.3
15.6
19.8
IRG VS et al. (2023)
7.4
12.4
29.4
SF-UT Hao et al. (2024)
6.7
11.8
28.0
REUNION ( ours )
8.4
11.1
34.6
Table 3: Comparison to SFDA baselines with different object sizes on Cityscapes-Adverse.
Table 6
Figure 3: Class-wise AP across weather conditions. Comparison of IRG VS et al. (2023) , SF-UT Hao et al. (2024) , and REUNION ; best and second-best results are highlighted in red and green , respectively.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Dataset
No. Classes
No. Images
Source
Adaptation
Evaluation
Cityscapes [ 6 ]
8
2,950
-
-
Sim10K [ 11 ]
1
9,975
-
-
Cityscapes → Cityscapes-Adverse
8
-
20,650
3,500
Sim10K → Cityscapes-Adverse
1
-
19,768
3,346
Appendix
Table A1: Dataset statistics.
Figure A1 : Class-wise instances distribution from Cityscapes dataset.
Type
Methods
D-Rainy
D-Snowy
D-Foggy
D-Sandstorm
N-Clear
N-Rainy
N-Foggy
mAP
Lower-Bound
Source-Only
15.9
16.1
20.8
3.0
9.2
6.4
5.5
11.0
Higher-Bound
Oracle
41.9
50.9
48.1
45.7
39.7
35.5
37.1
42.7
UDA
DAF [ 3 ]
25.0
23.2
32.8
30.0
18.6
11.1
15.3
22.3
SADAF [ 4 ]
21.6
22.9
33.7
27.4
17.0
9.0
15.2
20.1
PT [ 2 ]
23.6
21.5
35.9
28.3
18.6
16.0
14.7
22.7
MIC [ 9 ]
21.0
23.2
29.9
26.2
17.3
10.9
13.8
20.3
Appendix
Table A2 : Performance comparison on ResNet50 (in AP) of our method with other approaches for UDA/SFDA from Cityscapes to Cityscapes-Adverse.
Table 11
Baselines
Model
AP S
AP M
AP L
Vision Specialists
LPLD [ 24 ]
5.2
14.3
19.4
MT [ 20 ]
5.3
15.6
19.8
IRG [ 23 ]
7.4
12.4
29.4
SF-UT [ 8 ]
6.7
11.8
28.0
REUNION ( ours )
8.4
11.1
34.6
VLMs / MFMs
Grounding Dino [ 5 ]
0.0
2.5
19.2
Appendix
Table A5 : Quantitative Comparison of Object Detection Capabilities. The performance of our proposed method is compared against state-of-the-art Source-Free Domain Adaptation (SFDA) vision specialists and modern Vision-Language Models (VLMs) / Multi-modal Foundation Models (MFMs).
Figure A2 : Qualitative results for the Cityscapes-Adverse dataset are displayed from left to right, showcasing the raw image, followed by results from Grounding Dino, GPT-4.1, Gemini 2.5 Flash, Qwen 2.5-VL, Qwen 3.0-VL, Google OWLv2 and the proposed (Ours), with the ground truth (GT) presented on the far right.
School of Computing and Data Science, The University of Hong Kong · Department of Electrical & Computer Engineering, National University of Singapore · School of Information and Electronics, Beijing Institute of Technology