Semantically consistent cross-domain image translation facilitates the generation of training data by transferring labels across different domains, making it particularly useful for plant trait identification in agriculture. However, existing generative models struggle to maintain object-level accuracy when translating images between domains, especially when domain gaps are significant. In this work, we introduce AGILE (Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification), a diffusion-based framework that leverages optimized text embeddings and attention guidance to semantically constrain image translation. AGILE utilizes pretrained diffusion models and publicly available agricultural datasets to improve the fidelity of translated images while preserving critical object semantics. Our approach optimizes text embeddings to strengthen the correspondence between source and target images and guides attention maps during the denoising process to control object placement. We evaluate AGILE on cross-domain plant datasets and demonstrate its effectiveness in generating semantically accurate translated images. Quantitative experiments show that AGILE enhances object detection performance in the target domain while maintaining realism and consistency. Compared to prior image translation methods, AGILE achieves superior semantic alignment, particularly in challenging cases where objects vary significantly or domain gaps are substantial.
View on arXiv@article{ranario2025_2503.22019,
title={ AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification },
author={ Earl Ranario and Lars Lundqvist and Heesup Yun and Brian N. Bailey and J. Mason Earles },
journal={arXiv preprint arXiv:2503.22019},
year={ 2025 }
}