ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes

arXiv:2609.36891 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Pseudo-labels — 17× in this paper · explained in 2 episodes
• Clustering — 10× in this paper · explained in 2 episodes
• Instance Segmentation — 10× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Panoptic Quality — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Unsupervised Semantic Segmentation by Distilling Feature Correspondences
• GO: The Great Outdoors Multimodal Dataset
• Language-driven Semantic Segmentation
• Data or Language Supervision: What Makes CLIP Better than DINO?
• Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

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