91 citations · 274 across the 15 of their papers we have counts for
15 papers
Colorizing Monochromatic Radiance Fields
Yean Cheng, Renjie Wan, Shuchen Weng +3
Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic ima…
Gene-induced Multimodal Pre-training for Image-omic Classification
Ting Jin, Xingran Xie, Renjie Wan +2
Histology analysis of the tumor micro-environment integrated with genomic assays is the gold standard for most cancers in modern medicine. This paper proposes a Gene-induced Multim…
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…
Enhancing Low-Light Images Using Infrared-Encoded Images
Shulin Tian, Yufei Wang, Renjie Wan +3
Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum…
The Age of Synthetic Realities: Challenges and Opportunities
João Phillipe Cardenuto, Jing Yang, Rafael Padilha +7
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of…
Removing Image Artifacts From Scratched Lens Protectors
Yufei Wang, Renjie Wan, Wenhan Yang +3
A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones.…
Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment
Kecheng Chen, Jie Liu, Renjie Wan +4
Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning is widely used…
Enhancing Low-Light Images in Real World via Cross-Image Disentanglement
Lanqing Guo, Renjie Wan, Wenhan Yang +2
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e.g., real noise. Existing supervised enlightening algorithms require a large s…
Learning Meta Pattern for Face Anti-Spoofing
Rizhao Cai, Zhi Li, Renjie Wan +3
Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task hav…
Low-Light Image Enhancement with Normalizing Flow
Yufei Wang, Renjie Wan, Wenhan Yang +3
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise r…
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Haoliang Li, YuFei Wang, Renjie Wan +3
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require access…
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
Haoliang Li, Yufei Wang, Xiaofei Xie +5
Deep neural networks (DNN) have shown great success in many computer vision applications. However, they are also known to be susceptible to backdoor attacks. When conducting backdo…
Face Image Reflection Removal
Renjie Wan, Boxin Shi, Haoliang Li +2
Face images captured through the glass are usually contaminated by reflections. The non-transmitted reflections make the reflection removal more challenging than for general scenes…
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
Renjie Wan, Boxin Shi, Ling-Yu Duan +2
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcr…