186 citations · 801 across the 49 of their papers we have counts for
10 papers · 1 filter
FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
Rui Liu, Hanming Deng, Yangyi Huang +6
Transformer, as a strong and flexible architecture for modelling long-range relations, has been widely explored in vision tasks. However, when used in video inpainting that require…
Dual-Camera Super-Resolution with Aligned Attention Modules
Tengfei Wang, Jiaxin Xie, Wenxiu Sun +2
We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and…
A Categorized Reflection Removal Dataset with Diverse Real-world Scenes
Chenyang Lei, Xuhua Huang, Chenyang Qi +4
Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amou…
Efficient Regional Memory Network for Video Object Segmentation
Haozhe Xie, Hongxun Yao, Shangchen Zhou +2
Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for seg…
Decoupled Spatial-Temporal Transformer for Video Inpainting
Rui Liu, Hanming Deng, Yangyi Huang +6
Video inpainting aims to fill the given spatiotemporal holes with realistic appearance but is still a challenging task even with prosperous deep learning approaches. Recent works i…
Deep Animation Video Interpolation in the Wild
Li Siyao, Shiyu Zhao, Weijiang Yu +4
In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. Therefore, it is desirable to devel…
Exploiting Raw Images for Real-Scene Super-Resolution
Xiangyu Xu, Yongrui Ma, Wenxiu Sun +1
Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single ima…
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
Aojun Zhou, Yukun Ma, Junnan Zhu +5
Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into uns…
Semi-synthesis: A fast way to produce effective datasets for stereo matching
Ju He, Enyu Zhou, Liusheng Sun +3
Stereo matching is an important problem in computer vision which has drawn tremendous research attention for decades. Recent years, data-driven methods with convolutional neural ne…
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
Xiangyu Xu, Muchen Li, Wenxiu Sun +1
Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explici…