3 citations · 5 across the 5 of their papers we have counts for
5 papers
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…
Investigation of Factorized Optical Flows as Mid-Level Representations
Hsuan-Kung Yang, Tsu-Ching Hsiao, Ting-Hsuan Liao +7
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learnin…