5 citations · 11 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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.…
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…
MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
Xiaoguang Li, Qing Guo, Di Lin +3
Although achieving significant progress, existing deep generative inpainting methods are far from real-world applications due to the low generalization across different scenes. As…