57 citations · 133 across the 14 of their papers we have counts for
8 papers · 1 filter
CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
Ziyuan Luo, Qing Guo, Ka Chun Cheung +2
Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright sho…
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
Canyu Zhang, Qing Guo, Xiaoguang Li +4
In this work, we introduce a challenging image restoration task, referred to as SuperInpaint, which aims to reconstruct missing regions in low-resolution images and generate comple…
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection
Huiming Sun, Lan Fu, Jinlong Li +5
Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient…
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Rabab Abdelfattah, Qing Guo, Xiaoguang Li +2
This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference.…
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
Jin Ma, Jinlong Li, Qing Guo +3
The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB inpu…
Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal
Xiaoguang Li, Qing Guo, Pingping Cai +3
Shadow removal is to restore shadow regions to their shadow-free counterparts while leaving non-shadow regions unchanged. State-of-the-art shadow removal methods train deep neural…
Leveraging Inpainting for Single-Image Shadow Removal
Xiaoguang Li, Qing Guo, Rabab Abdelfattah +4
Fully-supervised shadow removal methods achieve the best restoration qualities on public datasets but still generate some shadow remnants. One of the reasons is the lack of large-s…
Structure-Informed Shadow Removal Networks
Yuhao Liu, Qing Guo, Lan Fu +5
Existing deep learning-based shadow removal methods still produce images with shadow remnants. These shadow remnants typically exist in homogeneous regions with low-intensity value…