Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation

arXiv:2406.09896 · cs.CV · Submitted 2024-06-14 · Read on arXiv

cs.CV

Submitted: 2024-06-14

Updated: 2026-09-25

Comments: CVPR 2024 Workshop Proceedings for the Second Workshop on Foundation Models

DOI: 10.1109/CVPRW63382.2024.00124

Code: https://github.com/tue-mps/vfmuda

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

Terminology

• Generalization — 38× in this paper · explained in 13 episodes
• Adaptation — 37× in this paper · explained in 1 episode
• Domain Adaptation — 21× in this paper · explained in 5 episodes
• Unsupervised Domain Adaptation — 13× in this paper
• Foundation Model — 11× in this paper · explained in 3 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Vision Foundation Model — 9× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Feature Distance — 2× in this paper
• Masked Image Consistency — 2× in this paper
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Domain Shift — 1× in this paper · explained in 4 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Mean Squared Error — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• EVA-02: A Visual Representation for Neon Genesis
• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation

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