3 papers
cs.CV2026
Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection
Lars Lundqvist, Earl Ranario, Hamid Kamangir +4
Vision foundation models (VFMs) offer the promise of zero-shot object detection without task-specific training data, yet their performance in complex agricultural scenes remains hi…
cs.CV2026
Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning
Heesup Yun, Isaac Kazuo Uyehara, Earl Ranario +4
This paper introduces a synthetic benchmark to evaluate the performance of vision language models (VLMs) in generating plant simulation configurations for digital twins. While func…
cs.CV2025
AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification
Earl Ranario, Lars Lundqvist, Heesup Yun +2
Semantically consistent cross-domain image translation facilitates the generation of training data by transferring labels across different domains, making it particularly useful fo…