58 citations · 118 across the 13 of their papers we have counts for
13 papers
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Yuxiang Wei, Zhilong Ji, Xiaohe Wu +3
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can…
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
Ruohao Wang, Xiaohui Liu, Zhilu Zhang +4
Video super-resolution (VSR) aiming to reconstruct a high-resolution (HR) video from its low-resolution (LR) counterpart has made tremendous progress in recent years. However, it r…
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
Ming Liu, Yuxiang Wei, Xiaohe Wu +2
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality. With the ability to g…
Learning Diverse Tone Styles for Image Retouching
Haolin Wang, Jiawei Zhang, Ming Liu +2
Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing…
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
Junyi Li, Xiaohe Wu, Zhenxing Niu +1
While significant progress has been made in deep video denoising, it remains very challenging for exploiting historical and future frames. Bidirectional recurrent networks (BiRNN)…
Invertible Network for Unpaired Low-light Image Enhancement
Jize Zhang, Haolin Wang, Xiaohe Wu +1
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 separa…
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
Yue Cao, Xiaohe Wu, Shuran Qi +3
The success of deep denoisers on real-world color photographs usually relies on the modeling of sensor noise and in-camera signal processing (ISP) pipeline. Performance drop will i…
Unpaired Learning of Deep Image Denoising
Xiaohe Wu, Ming Liu, Yue Cao +2
We investigate the task of learning blind image denoising networks from an unpaired set of clean and noisy images. Such problem setting generally is practical and valuable consider…
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Feng Li, Xiaohe Wu, Wangmeng Zuo +2
Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Most of the existing…
Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking
Yingjie Yao, Xiaohe Wu, Lei Zhang +2
Correlation filter (CF) based trackers generally include two modules, i.e., feature representation and on-line model adaptation. In existing off-line deep learning models for CF tr…
VITAL: VIsual Tracking via Adversarial Learning
Yibing Song, Chao Ma, Xiaohe Wu +6
The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as…
Learning Support Correlation Filters for Visual Tracking
Wangmeng Zuo, Xiaohe Wu, Liang Lin +2
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. Re…
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
Xiaohe Wu, Wangmeng Zuo, Yuanyuan Zhu +1
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 ignor…