What is the Added Value of UDA in the VFM Era?

arXiv:2504.18190 · cs.CV · Submitted 2025-04-25 · Read on arXiv

cs.CV

Submitted: 2025-04-25

Updated: 2026-09-25

Terminology

• Fine-Tuning — 48× in this paper · explained in 5 episodes
• Generalization — 38× in this paper · explained in 13 episodes
• Adaptation — 29× in this paper · explained in 1 episode
• Domain Adaptation — 16× in this paper · explained in 5 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Autonomous Driving — 13× in this paper
• Unsupervised Domain Adaptation — 9× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Semi-supervised Semantic Segmentation — 7× in this paper
• Pseudo-labels — 6× in this paper · explained in 2 episodes
• Pseudo-labeling — 5× in this paper · explained in 3 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 6 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Domain Generalized Semantic Segmentation — 2× in this paper
• Domain Shift — 2× in this paper · explained in 4 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Masked Image Consistency — 2× in this paper
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• alpha — 2× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation
• 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
• Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier
• CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
• Harnessing Diffusion Models for Visual Perception with Meta Prompts
• First Place Solution to the ECCV 2024 BRAVO Challenge: Evaluating Robustness of Vision Foundation Models for Semantic Segmentation
• Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing

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