116 citations · 131 across the 5 of their papers we have counts for
5 papers
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…
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng +6
Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods f…
CLCC: Contrastive Learning for Color Constancy
Yi-Chen Lo, Chia-Che Chang, Hsuan-Chao Chiu +4
In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations f…
One-Shot Object Detection with Co-Attention and Co-Excitation
Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen +1
This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the ta…