12 citations · 14 across the 3 of their papers we have counts for
3 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…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…