Z
Zhi Jin
Publications - 5
Citations - 49
Zhi Jin is an academic researcher. The author has contributed to research in topics: Computer science & Image (mathematics). The author has an hindex of 1, co-authored 1 publications receiving 22 citations.
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Proceedings ArticleDOI
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr,Martin Danelljan,Radu Timofte,Christoph Busch,Yang Chen,Jian Cheng,Vishal Chudasama,Ruipeng Gang,Shangqi Gao,Kun Gao,Laiyun Gong,Qingrui Han,Chao Huang,Zhi Jin,Younghyun Jo,Seon Joo Kim,Younggeun Kim,Seungjun Lee,Yuchen Lei,Chu-Tak Li,Chenghua Li,Ke Li,Zhi-Song Liu,Youming Liu,Nan Nan,Seung-Ho Park,Heena Patel,Shichong Peng,Kalpesh Prajapati,Haoran Qi,Kiran B. Raja,Raghavendra Ramachandra,Wan-Chi Siu,Donghee Son,Ruixia Song,Kishor P. Upla,Li-Wen Wang,Yatian Wang,Junwei Wang,Qianyu Wu,Xinhua Xu,Sejong Yang,Zhen Yuan,Liting Zhang,Huanrong Zhang,Junkai Zhang,Yifan Zhang,Zhenzhou Zhang,Hangqi Zhou,Aichun Zhu,Xiahai Zhuang,Jiaxin Zou +51 more
TL;DR: The NTIRE 2021 challenge as mentioned in this paper addressed the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image.
Journal ArticleDOI
Depth-guided asymmetric CycleGAN for rain synthesis and image deraining
TL;DR: Extensive experiments indicate that the DA-CycleGAN can synthesize more lifelike rain images and provide commensurate deraining performance compared with the state-of-the-art deraining methods.
Journal ArticleDOI
An Efficient Latent Style Guided Transformer-CNN Framework for Face Super-Resolution
Haoran Qi,Yuwei Qiu,Zhi Jin +2 more
TL;DR: Li et al. as mentioned in this paper proposed an Efficient Latent Style guided Transformer-CNN framework for face super-resolution, which consists of a Feature Preparation Stage and a Feature Carving Stage.
Journal ArticleDOI
V-DixMatch: A Semi-Supervised Learning Method for Human Action Recognition in Night Video Sensing
Journal ArticleDOI
Self-distillation framework for indoor and outdoor monocular depth estimation
TL;DR: This work designs a student encoder that extracts features from two datasets of indoor and outdoor scenes, respectively, and introduces a dissimilarity loss to pull apart encoded features of different scenes in the feature space, and proposes a self-distillation MDE framework to improve the generalization ability across scenes.