Yu-Lun Liu

I am a fourth year PhD student working with Yung-Yu Chuang in the CSIE department at National Taiwan University. I work on problems in computer vision, machine learning, and multimedia.

I am also a senior algorithm development engineer at MediaTek Inc., where I work on computational photography, computer vision, and machine learning.

I did my undergrad and bachelors at National Chiao Tung University.

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Research


Explorable Tone Mapping Operators
Chien-Chuan Su, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Soo-Chang Pei
Proceedings of the 25th International Conference on Pattern Recognition (ICPR), 2020  
arXiv

In this paper, a learning-based multimodal tone-mapping method is proposed, which not only achieves excellent visual quality but also explores the style diversity.


Learning Camera-Aware Noise Models
Ke-Chi Chang, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Hwann-Tzong Chen
Proceedings of European Conference on Computer Vision (ECCV), 2020  
project page / arXiv / code

We propose a data-driven approach, where a generative noise model is learned from real-world noise.


Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
Yu-Lun Liu*, Wei-Sheng Lai*, Yu-Sheng Chen, Yi-Lung Kao, Min-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020  
project page / arXiv / code / demo

In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model.



Learning to See Through Obstructions
Yu-Lun Liu, Wei-Sheng Lai, Min-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020  
project page / arXiv / code / demo / video / New Scientists

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.


Attention-based View Selection Networks for Light-field Disparity Estimation
Yu-Ju Tsai, Yu-Lun Liu, Yung-Yu Chuang, Ming Ouhyoung
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2020  
paper / code / benchmark

For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation.


Deep Video Frame Interpolation using Cyclic Frame Generation
Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, Yung-Yu Chuang
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2019   (Oral Presentation)
project page / paper / code

The cycle consistency loss can better utilize the training data to not only enhance the interpolation results, but also maintain the performance better with less training data.

Background modeling using depth information
Yu-Lun Liu, Hsueh-Ming Hang
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014  
paper

This paper mainly focuses on creating a global background model of a video sequence using the depth maps together with the RGB pictures.

Virtual view synthesis using backward depth warping algorithm
Du-Hsiu Li, Hsueh-Ming Hang, Yu-Lun Liu
Picture Coding Symposium (PCS), 2013  
paper

In this study, we propose a backward warping process to replace the forward warping process, and the artifacts (particularly the ones produced by quantization) are significantly reduced.

Service
Emergency reviewer, ECCV 2020

Reviewer, ACCV 2020

Reviewer, AAAI 2021

Reviewer, IJCAI 2021

Reviewer, ICCV 2021

Reviewer, ICLR 2022

Reviewer, IJCAI 2022

Reviewer, Applied Soft Computing

Stolen from Jon Barron's website.
Last updated December 2020.