activity
20192022
most citedNoise-resistant Deep Metric Learning with Ranking-based Instance Selection

6 citations · 7 across the 6 of their papers we have counts for

collaborators

11 papers

cs.CV2022

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li, Kai Zhang, Radu Timofte +108

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…

cs.CV2022

Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image Transition

Tao Yang, Peiran Ren, Xuansong Xie +2

Video frame interpolation (VFI) aims to improve the temporal resolution of a video sequence. Most of the existing deep learning based VFI methods adopt off-the-shelf optical flow a…

cs.CV2021

GAN Prior Embedded Network for Blind Face Restoration in the Wild

Tao Yang, Peiran Ren, Xuansong Xie +1

Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradatio…

cs.CV2021

Attention-guided Temporally Coherent Video Object Matting

Yunke Zhang, Chi Wang, Miaomiao Cui +6

This paper proposes a novel deep learning-based video object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal a…

cs.CV20216 cited

Noise-resistant Deep Metric Learning with Ranking-based Instance Selection

Chang Liu, Han Yu, Boyang Li +6

The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving robustness…

cs.CV2020

Intrinsic Temporal Regularization for High-resolution Human Video Synthesis

Lingbo Yang, Zhanning Gao, Peiran Ren +2

Temporal consistency is crucial for extending image processing pipelines to the video domain, which is often enforced with flow-based warping error over adjacent frames. Yet for hu…