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Found 3 papers, 2 papers with code
DARTS: Double Attention Reference-based Transformer for Super-resolution
Our work demonstrates how the attention mechanism can be adapted for the particular requirements of reference-based image super-resolution, significantly simplifying the architecture and training pipeline.
Unsupervised Domain Adaptation For Plant Organ Counting
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.
AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images
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 literature using state-of-the-art supervised deep learning techniques.
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