학술논문
NTIRE 2023 Video Colorization Challenge
Document Type
Conference
Author
Kang, Xiaoyang; Lin, Xianhui; Zhang, Kai; Hui, Zheng; Xiang, Wangmeng; He, Jun-Yan; Li, Xiaoming; Ren, Peiran; Xie, Xuansong; Timofte, Radu; Yang, Yixin; Pan, Jinshan; Zheng, Zhong; Qiyan, Peng; Jiangxin, Zhang; Jinhui, Dong; Jinjing, Tang; Chichen, Li; Li, Lin Qipei; Liang, Qirong; Gang, Ruipeng; Liu, Xiaofeng; Feng, Shuang; Liu, Shuai; Wang, Hao; Feng, Chaoyu; Bai, Furui; Zhang, Yuqian; Shao, Guangqi; Wang, Xiaotao; Lei, Lei; Chen, Siqi; Zhang, Yu; Xu, Hanning; Liu, Zheyuan; Zhang, Zhao; Luo, Yan; Zuo, Zhichao
Source
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) CVPRW Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 IEEE/CVF Conference on. :1570-1581 Jun, 2023
Subject
Language
ISSN
2160-7516
Abstract
This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inception Distance) while being constrained to maintain or improve over the baseline method in terms of the temporal-consistency metric. The Color Distribution Consistency (CDC) index is used as the temporal consistency evaluation metric in this challenge. For Track 2, the target is to obtain a solution with the best CDC result while being constrained to maintain or improve over the baseline method in terms of FID. We use DeOldify-video as the baseline method for two tracks. For the final testing phase of both tracks, six teams submitted fact sheets and executable code of their solutions. This report brings together descriptions and discussions of all these solutions. Both tracks use the same data and the datasets are available at this url.