4 papers
From Semantic To Instance: A Semi-Self-Supervised Learning Approach
Keyhan Najafian, Farhad Maleki, Lingling Jin +1
Instance segmentation is essential for applications such as automated monitoring of plant health, growth, and yield. However, extensive effort is required to create large-scale dat…
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture
Andrew Heschl, Mauricio Murillo, Keyhan Najafian +1
This paper introduces a methodology for generating synthetic annotated data to address data scarcity in semantic segmentation tasks within the precision agriculture domain. Utilizi…
A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation
Keyhan Najafian, Farhad Maleki, Lingling Jin +1
Video object segmentation (VOS) -- predicting pixel-level regions for objects within each frame of a video -- is particularly challenging in agricultural scenarios, where videos of…
Modified CycleGAN for the synthesization of samples for wheat head segmentation
Jaden Myers, Keyhan Najafian, Farhad Maleki +1
Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily…