Latent Commonality Expectation-Maximisation for Box-supervised Tree Crown Instance Segmentation

arXiv:2609.26549 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/facebookresearch/detectron2

Terminology

• Instance Segmentation — 29× in this paper · explained in 3 episodes
• Benchmark — 20× in this paper · explained in 2 episodes
• Tree Crown Detection — 10× in this paper
• Tree Crown Instance Segmentation — 10× in this paper
• Remote Sensing — 8× in this paper
• Transformer — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Embeddings — 5× in this paper · explained in 2 episodes
• Foundation Model — 5× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 4× in this paper
• Decode — 4× in this paper · explained in 2 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Pruning — 4× in this paper · explained in 4 episodes
• Region Proposal Network — 4× in this paper
• Ground Sample Distance — 3× in this paper
• Mask Generation — 3× in this paper · explained in 2 episodes
• Segment Anything Model — 3× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Feature Pyramid Network — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• National Ecological Observatory Network — 2× in this paper
• Segment Anything Model (SAM) — 2× in this paper · explained in 2 episodes
• Self-distillation — 2× in this paper · explained in 6 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Additivity — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 1× in this paper
• False Positives — 1× in this paper
• IEEE International Conference on Computer Vision — 1× in this paper
• In International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• International Joint Conference on Artificial Intelligence — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• 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
• World Model — 1× in this paper · explained in 7 episodes

Sources

• SelvaBox: A high-resolution dataset for tropical tree crown detection
• SelvaMask: Segmenting Trees in Tropical Forests and Beyond
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
• DINOv2: Learning Robust Visual Features without Supervision
• DINOv3
• Objects as Points
• Unsupervised Semantic Segmentation by Distilling Feature Correspondences
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• SAM 3: Segment Anything with Concepts
• Perception Encoder: The best visual embeddings are not at the output of the network
• iBOT: Image BERT Pre-Training with Online Tokenizer

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