76 citations · 124 across the 8 of their papers we have counts for
8 papers
Improving Complex Moiré Removal with Generative Supervision
Xinyang Gu, Zhilu Zhang, Honglei Xu +3
The availability of high-quality paired data is essential for training learning-based image demoiréing models. However, it remains challenging for existing datasets to encompass th…
Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models
Zitong Huang, Kaidong Zhang, Yukang Ding +4
Aligning text-to-video diffusion models with human preferences is crucial for generating high-quality videos. Existing Direct Preference Otimization (DPO) methods rely on multi-sam…
Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution
Guandu Liu, Yukang Ding, Mading Li +3
Look-up table(LUT)-based methods have shown the great efficacy in single image super-resolution (SR) task. However, previous methods ignore the essential reason of restricted recep…
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
Kai Zhang, Shuhang Gu, Radu Timofte +60
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input im…
Dynamic Instance Normalization for Arbitrary Style Transfer
Yongcheng Jing, Xiao Liu, Yukang Ding +4
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defi…
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
Chao Li, Dongliang He, Xiao Liu +2
Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. However, there has been li…
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Ming Liu, Yukang Ding, Min Xia +4
Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually…
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Hongliang Yan, Yukang Ding, Peihua Li +3
In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-b…