activity
20182022
most citedMuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution

15 citations · 42 across the 6 of their papers we have counts for

collaborators

12 papers

cs.CV20221 cited

H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions

Changlin Li, Guangyang Wu, Yanan Sun +3

Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real…

cs.CV202213 cited

DeViT: Deformed Vision Transformers in Video Inpainting

Jiayin Cai, Changlin Li, Xin Tao +2

This paper proposes a novel video inpainting method. We make three main contributions: First, we extended previous Transformers with patch alignment by introducing Deformed Patch-b…

cs.CV20223 cited

Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling

Takashi Isobe, Xu Jia, Xin Tao +6

Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensat…

cs.CV20219 cited

MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution

Liying Lu, Wenbo Li, Xin Tao +2

Reference-based image super-resolution (RefSR) has shown promising success in recovering high-frequency details by utilizing an external reference image (Ref). In this task, textur…

cs.CV202015 cited

MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution

Wenbo Li, Xin Tao, Taian Guo +3

Video super-resolution (VSR) aims to utilize multiple low-resolution frames to generate a high-resolution prediction for each frame. In this process, inter- and intra-frames are th…

cs.CV2020

VCNet: A Robust Approach to Blind Image Inpainting

Yi Wang, Ying-Cong Chen, Xin Tao +1

Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image. Previous works assume missing region patterns are known…