ICM: Intra-class Mixing for Domain Adaptation in Adverse Weather

arXiv:2609.27533 · cs.CV · Submitted 2026-09-23 · Read on arXiv

cs.CV

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Adaptation — 38× in this paper · explained in 1 episode
• Consistency — 35× in this paper · explained in 3 episodes
• Domain Adaptation — 23× in this paper · explained in 5 episodes
• Unsupervised Domain Adaptation — 15× in this paper
• Intra-Class Mixing — 11× in this paper
• Regularization — 10× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Pseudo-labels — 6× in this paper · explained in 2 episodes
• Autonomous Driving — 5× in this paper
• Domain Shift — 4× in this paper · explained in 4 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Exponential Moving Average — 1× in this paper
• Masked Image Consistency — 1× in this paper
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
• Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
• Semi-supervised semantic segmentation needs strong, varied perturbations
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
• PatchShuffle Regularization
• Structured Consistency Loss for semi-supervised semantic segmentation
• Decoupled Weight Decay Regularization
• DINOv3

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