mlsanityData to 14 Oct 2026

Collaborating Foundation Models for Domain Generalized Semantic Segmentation

arXiv 2312.09788 ↗First posted 15 Dec 2023cs.CV cs.AI cs.LGcited by 34

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Abstract (arXiv)

Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates FMs of various kinds: (i) CLIP backbone for its robust feature representation, (ii) generative models to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6% and 6.7% on averaged miou, respectively. The code is available at : https://github.com/yasserben/CLOUDS

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 69 of its 85 references that are in the map. First posted on arXiv 15 Dec 2023.

Builds on

69 of 85 references.

References · 69

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38 more
  1. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 21; 2026: 9.

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  1. 2024SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learningcited by 24
  2. 2025Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentationcited by 24
  3. 2025Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Modelscited by 23
  4. 2025Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detectioncited by 9
  5. 2024CATALOG: A Camera Trap Language-guided Contrastive Learning Modelcited by 6
  6. 2025BlurDM: A Blur Diffusion Model for Image Deblurringcited by 5
  7. 2025Exploring Probabilistic Modeling Beyond Domain Generalization for Semantic Segmentationcited by 4
  8. 2025Evaluating SAM2 for Video Semantic Segmentationcited by 4
  9. 2025Training-Free Synthetic Data Generation with Dual IP-Adapter Guidancecited by 3
  10. 2025Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentationcited by 2
  11. 2025Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Modelscited by 2
  12. 2024VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptationcited by 1
22 more
  1. 2025Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentationcited by 1
  2. 2026Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentationcited by 1
  3. 2026HNDiff: Haze-Noise Diffusion for Image Dehazingcited by 1
  4. 2024A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Modelscited by 0
  5. 2025Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancementcited by 0
  6. 2025Set Pivot Learning: Redefining Generalized Segmentation with Vision Foundation Modelscited by 0
  7. 2025Deeply Dual Supervised learning for melanoma recognitioncited by 0
  8. 2025Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsamplingcited by 0
  9. 2025DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentationcited by 0
  10. 2025VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagationcited by 0
  11. 2025IELDG: Suppressing Domain-Specific Noise with Inverse Evolution Layers for Domain Generalized Semantic Segmentationcited by 0
  12. 2025Domain Generalization for Semantic Segmentation: A Surveycited by 0
  13. 2025Do We Need Perfect Data? Leveraging Noise for Domain Generalized Segmentationcited by 0
  14. 2025SAGE: Style-Adaptive Generalization for Privacy-Constrained Semantic Segmentation Across Domainscited by 0
  15. 2025Test-Time Modification: Inverse Domain Transformation for Robust Perceptioncited by 0
  16. 2026BLO-Inst: Bi-Level Optimization Based Alignment of YOLO and SAM for Robust Instance Segmentationcited by 0
  17. 2026RecycleLoRA: Rank-Revealing QR-Based Dual-LoRA Subspace Adaptation for Domain Generalized Semantic Segmentationcited by 0
  18. 2026Do Instance Priors Help Weakly Supervised Semantic Segmentation?cited by 0
  19. 2026Dual-Foundation Models for Unsupervised Domain Adaptationcited by 0
  20. 2026LASA: Language-and-Source-Anchored Alignment for Domain Generalized Semantic Segmentationcited by 0
  21. 2026Hierarchical Prompt Injector for Domain Generalization Segmentationcited by 0
  22. 2026Sparse All-Layer Connector for Domain Generalised Semantic Segmentationcited by 0