3 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung +4
Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noi…
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang +4
Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during ima…
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo +3
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with…
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Ting-Hsuan Liao, Huang-Ru Liao, Shan-Ya Yang +8
Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such i…