no code implementations • 14 Sep 2023 • Yang Li, Fan Zhong, Xin Wang, Shuangbing Song, Jiachen Li, Xueying Qin, Changhe Tu
The limitations of previous scoring methods and error metrics are analyzed, based on which we introduce our improved evaluation methods.
no code implementations • 13 Jul 2023 • Shuangbing Song, Fan Zhong, Tianju Wang, Xueying Qin, Changhe Tu
We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing.
1 code implementation • 26 Jul 2022 • Xuhui Tian, Xinran Lin, Fan Zhong, Xueying Qin
Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements.
no code implementations • CVPR 2022 • Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin
Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.
1 code implementation • Pacific Graphics 2020 • Hong Huang, Fan Zhong, Yuqing Sun, Xueying Qin
However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions.
no code implementations • arXiv 2019 • Jichao Zhang, Meng Sun, Jingjing Chen, Hao Tang, Yan Yan, Xueying Qin, Nicu Sebe
Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem.
no code implementations • 27 Aug 2018 • Xibin Song, Yuchao Dai, Xueying Qin
However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used; and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as $\times 8, \times 16$.
2 code implementations • 19 May 2018 • Jichao Zhang, Yezhi Shu, Songhua Xu, Gongze Cao, Fan Zhong, Meng Liu, Xueying Qin
To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.
no code implementations • 7 Jul 2016 • Xibin Song, Yuchao Dai, Xueying Qin
In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution.