Search Results for author: Xiaohe Wu

Found 12 papers, 6 papers with code

A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration

1 code implementation21 Jul 2022 Ming Liu, Yuxiang Wei, Xiaohe Wu, WangMeng Zuo, Lei Zhang

Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality.

Image Generation Image Restoration

Learning Diverse Tone Styles for Image Retouching

1 code implementation12 Jul 2022 Haolin Wang, Jiawei Zhang, Ming Liu, Xiaohe Wu, WangMeng Zuo

In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass.

Image Retouching

Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones

1 code implementation12 Apr 2022 Junyi Li, Xiaohe Wu, Zhenxing Niu, WangMeng Zuo

However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption.

Denoising Video Denoising +1

Invertible Network for Unpaired Low-light Image Enhancement

no code implementations24 Dec 2021 Jize Zhang, Haolin Wang, Xiaohe Wu, WangMeng Zuo

Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately.

Low-Light Image Enhancement

Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser

1 code implementation18 Mar 2021 Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, WangMeng Zuo

To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images.

Denoising

Unpaired Learning of Deep Image Denoising

2 code implementations ECCV 2020 Xiaohe Wu, Ming Liu, Yue Cao, Dongwei Ren, WangMeng Zuo

As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set.

Image Denoising Knowledge Distillation +1

Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How

1 code implementation16 May 2019 Feng Li, Xiaohe Wu, WangMeng Zuo, David Zhang, Lei Zhang

Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization.

Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking

no code implementations ECCV 2018 Yingjie Yao, Xiaohe Wu, Lei Zhang, Shiguang Shan, WangMeng Zuo

In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an end-to-end manner.

VITAL: VIsual Tracking via Adversarial Learning

no code implementations CVPR 2018 Yibing Song, Chao Ma, Xiaohe Wu, Lijun Gong, Linchao Bao, WangMeng Zuo, Chunhua Shen, Rynson Lau, Ming-Hsuan Yang

To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.

General Classification Visual Tracking

Learning Support Correlation Filters for Visual Tracking

no code implementations22 Jan 2016 Wangmeng Zuo, Xiaohe Wu, Liang Lin, Lei Zhang, Ming-Hsuan Yang

Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency.

Visual Tracking

F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

no code implementations20 Apr 2015 Xiaohe Wu, WangMeng Zuo, Yuanyuan Zhu, Liang Lin

The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius.

Cannot find the paper you are looking for? You can Submit a new open access paper.