2 citations · 2 across the 3 of their papers we have counts for
4 papers
HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution
Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness
In this paper, we propose HiTSR, a hierarchical transformer model for reference-based image super-resolution, which enhances low-resolution input images by learning matching corres…
DARTS: Double Attention Reference-based Transformer for Super-resolution
Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness
We present DARTS, a transformer model for reference-based image super-resolution. DARTS learns joint representations of two image distributions to enhance the content of low-resolu…
Unsupervised Domain Adaptation For Plant Organ Counting
Tewodros Ayalew, Jordan Ubbens, Ian Stavness
Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting pl…
AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
Jordan Ubbens, Tewodros Ayalew, Steve Shirtliffe +3
Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the…