VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation

arXiv:2503.10685 · cs.CV, eess.IV · Submitted 2025-03-11 · Read on arXiv

cs.CV, eess.IV

Submitted: 2025-03-11

Updated: 2026-09-25

DOI: 10.1109/ICCVW69036.2025.11495570

Code: https://github.com/tue-mps/vfm-udaplusplus

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 34× in this paper · explained in 13 episodes
• Adaptation — 19× in this paper · explained in 1 episode
• Benchmark — 18× in this paper · explained in 2 episodes
• Domain Adaptation — 11× in this paper · explained in 5 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Unsupervised Domain Adaptation — 8× in this paper
• Foundation Model — 7× in this paper · explained in 3 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Domain Shift — 3× in this paper · explained in 4 episodes
• Pseudo-labels — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Computer Vision and Pattern Recognition — 2× in this paper
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Exponential Moving Average — 2× in this paper
• Exponential Moving Average (EMA) — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper

Sources

• EVA-02: A Visual Representation for Neon Genesis
• Semi-supervised semantic segmentation needs strong, varied perturbations
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
• Deep Learning using Rectified Linear Units (ReLU)
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
• Harnessing Diffusion Models for Visual Perception with Meta Prompts
• Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
• SAM 2: Segment Anything in Images and Videos

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