Vision-Language-Guided Pseudo-Labels for Unsupervised Domain Adaptation in Semantic Segmentation for Waste Sorting

arXiv:2609.00898 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-01 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/lmu-dbs/cross-modal-pseudo-labeling-pipeline

Terminology

• Adaptation — 49× in this paper · explained in 1 episode
• Pseudo-labels — 40× in this paper · explained in 2 episodes
• Domain Adaptation — 30× in this paper · explained in 5 episodes
• Pseudo-labeling — 29× in this paper · explained in 3 episodes
• Verification — 20× in this paper · explained in 2 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Domain Shift — 14× in this paper · explained in 4 episodes
• Verifier — 14× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Foundation Model — 6× in this paper · explained in 3 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 4× in this paper
• Consistency — 4× in this paper · explained in 3 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Class-Balanced Self-Training — 3× in this paper
• Adversarial Training — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Pseudo Label Self-Refinement — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Segment Anything Model — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Computational Biology — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Rethinking Atrous Convolution for Semantic Image Segmentation
• Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
• EVA-CLIP: Improved Training Techniques for CLIP at Scale

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