Scalable In-Domain Self-Supervised Foundation Model for Dense Representation Transfer in High-Resolution Plant Imaging

arXiv:2609.32183 · cs.CV, eess.IV · Submitted 2026-09-26 · Read on arXiv

cs.CV, eess.IV

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/junlinguo/PlantVFM-DenseBench

Terminology

• Foundation Model — 16× in this paper · explained in 3 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Foundation Models — 8× in this paper · explained in 7 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Self-Supervised Learning — 5× in this paper · explained in 8 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• AI Foundation Model — 1× in this paper · explained in 1 episode
• Activation Checkpointing — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• DE-AC05-00OR22725 with the US Department of Energy — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Masked Autoencoder (MAE) — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• BEiT: BERT Pre-Training of Image Transformers
• iBOT: Image BERT Pre-Training with Online Tokenizer
• DINOv2: Learning Robust Visual Features without Supervision
• DINOv3
• Virchow: A Million-Slide Digital Pathology Foundation Model

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