no code implementations • 31 Jan 2018 • Guangyu Zhong, Yi-Hsuan Tsai, Sifei Liu, Zhixun Su, Ming-Hsuan Yang
In this paper, we propose a learning-based method to compose a video-story from a group of video clips that describe an activity or experience.
no code implementations • 25 Dec 2017 • Yang Liu, Jinshan Pan, Zhixun Su
However, directly using exist- ing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions.
no code implementations • 28 Feb 2017 • Yiyang Wang, Risheng Liu, Xiaoliang Song, Zhixun Su
In recent years, numerous vision and learning tasks have been (re)formulated as nonconvex and nonsmooth programmings(NNPs).
no code implementations • 28 May 2016 • Risheng Liu, Jing Wang, Yiyang Wang, Zhixun Su, Yu Cai
In this paper, we propose a novel sparse coding and counting method under Bayesian framwork for visual tracking.
no code implementations • 5 Dec 2012 • Jinshan Pan, Risheng Liu, Zhixun Su, Xianfeng GU
One effective way to eliminate these details is to apply image denoising model based on the Total Variation (TV).
no code implementations • 26 Aug 2011 • Risheng Liu, Zhouchen Lin, Siming Wei, Zhixun Su
In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$.
no code implementations • NeurIPS 2011 • Zhouchen Lin, Risheng Liu, Zhixun Su
It suffers from $O(n^3)$ computation complexity due to the matrix-matrix multiplications and matrix inversions, even if partial SVD is used.
Optimization and Control
no code implementations • ECCV 2018 • Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, Ming-Hsuan Yang
We propose a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers.
no code implementations • CVPR 2014 • Jinshan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang
We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring.
no code implementations • CVPR 2016 • Jinshan Pan, Zhe Hu, Zhixun Su, Hsin-Ying Lee, Ming-Hsuan Yang
To address these problems, we propose a novel model for object motion deblurring.
no code implementations • CVPR 2016 • Jinshan Pan, Zhouchen Lin, Zhixun Su, Ming-Hsuan Yang
Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present.
no code implementations • ICCV 2017 • Jinshan Pan, Jiangxin Dong, Yu-Wing Tai, Zhixun Su, Ming-Hsuan Yang
Solving blind image deblurring usually requires defining a data fitting function and image priors.
no code implementations • ICCV 2017 • Jiangxin Dong, Jinshan Pan, Zhixun Su, Ming-Hsuan Yang
We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally.
no code implementations • 20 Mar 2019 • Haohao Li, Shengfa Wang, Nannan Li, Zhixun Su, Ximin Liu
The different intrinsic representations (features) focus on different geometric properties to describe the same 3D shape, which makes the representations are related.
no code implementations • 30 Mar 2020 • Honghe Zhu, Cong Wang, Ya-Jie Zhang, Zhixun Su, Guohui Zhao
Single image deraining is an urgent task because the degraded rainy image makes many computer vision systems fail to work, such as video surveillance and autonomous driving.
no code implementations • 3 Aug 2020 • Cong Wang, Xiaoying Xing, Zhixun Su, Junyang Chen
Further, we design an inner-scale connection block to utilize the multi-scale information and features fusion way between different scales to improve rain representation ability and we introduce the dense block with skip connection to inner-connect these blocks.
no code implementations • ICCV 2021 • Yang Liu, Ziyu Yue, Jinshan Pan, Zhixun Su
With the estimated rain maps from the semi-supervised learning part, we first synthesize a new paired set by adding to rain-free images based on the superimposition model.
no code implementations • 8 May 2023 • Cheng Yang, Lijing Liang, Zhixun Su
We introduce a diffusion process with linear interpolation, and the intermediate noisy image is interpolated from the original clean image and the corresponding real-world noisy image, so that this diffusion model can handle the level of added noise.
no code implementations • 29 Feb 2024 • Ziyu Yue, Jiaxin Gao, Sihan Xie, Yang Liu, Zhixun Su
The visibility of real-world images is often limited by both low-light and low-resolution, however, these issues are only addressed in the literature through Low-Light Enhancement (LLE) and Super- Resolution (SR) methods.
1 code implementation • 28 Jun 2020 • Junqi Lin, Huixin Miao, Junjie Cao, Zhixun Su, Risheng Liu
Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches.
Ranked #7 on Multi-Person Pose Estimation on COCO test-dev
1 code implementation • 17 Aug 2023 • Liyan Wang, Qinyu Yang, Cong Wang, Wei Wang, Jinshan Pan, Zhixun Su
Specifically, our C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training scheme.
1 code implementation • 6 Aug 2020 • Cong Wang, Yutong Wu, Zhixun Su, Junyang Chen
In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions.
1 code implementation • 4 Dec 2013 • Yangyang Xu, Ruru Hao, Wotao Yin, Zhixun Su
Phase transition plots reveal that our algorithm can recover a variety of synthetic low-rank tensors from significantly fewer samples than the compared methods, which include a matrix completion method applied to tensor recovery and two state-of-the-art tensor completion methods.
Numerical Analysis Numerical Analysis Computation