102 citations · 340 across the 23 of their papers we have counts for
8 papers · 1 filter
NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework
Yuan Tong, Mengshun Hu, Zheng Wang
We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enh…
TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting
Taorong Liu, Liang Liao, Delin Chen +4
Image inpainting for completing complicated semantic environments and diverse hole patterns of corrupted images is challenging even for state-of-the-art learning-based inpainting m…
UmeTrack: Unified multi-view end-to-end hand tracking for VR
Shangchen Han, Po-chen Wu, Yubo Zhang +13
Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. Existing work in this space are limited to either producin…
Magic ELF: Image Deraining Meets Association Learning and Transformer
Kui Jiang, Zhongyuan Wang, Chen Chen +3
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications. However, little effort has been made to effectively and efficiently harmo…
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution
Mengshun Hu, Kui Jiang, Zhixiang Nie +1
Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR…
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning
Mengshun Hu, Kui Jiang, Liang Liao +3
Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate super-resolved videos with higher resolution(HR) and higher frame rate (HFR). Quite intuitively, pioneering two-st…
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding
Xianzheng Ma, Zhixiang Wang, Yacheng Zhan +4
Although considerable progress has been made in semantic scene understanding under clear weather, it is still a tough problem under adverse weather conditions, such as dense fog, d…
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Kui Jiang, Zhongyuan Wang, Zheng Wang +4
Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness. Different from existing methods tending to accomplish the relighting task di…