no code implementations • 9 Mar 2023 • Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo, Roy Tseng, Chia-Che Chang, Chun-Yi Lee
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 the normalizing flow.
1 code implementation • 16 Nov 2022 • Ting-Hsuan Liao, Huang-Ru Liao, Shan-Ya Yang, Jie-En Yao, Li-Yuan Tsao, Hsu-Shen Liu, Bo-Wun Cheng, Chen-Hao Chao, Chia-Che Chang, Yi-Chen Lo, Chun-Yi Lee
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
1 code implementation • ICLR 2022 • Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
1 code implementation • CVPR 2021 • Yi-Chen Lo, Chia-Che Chang, Hsuan-Chao Chiu, Yu-Hao Huang, Chia-Ping Chen, Yu-Lin Chang, Kevin Jou
In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
2 code implementations • NeurIPS 2019 • Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu
This paper aims to tackle the challenging problem of one-shot object detection.
Ranked #2 on One-Shot Object Detection on COCO