17 citations · 39 across the 18 of their papers we have counts for
7 papers · 1 filter
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
Jing Hu, Qinrui Fan, Shu Hu +3
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images…
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection
Lei Zhang, Hao Chen, Shu Hu +5
Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive im…
CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition
Shanmin Yang, Hui Guo, Shu Hu +5
Deepfake technology poses a significant threat to security and social trust. Although existing detection methods have shown high performance in identifying forgeries within dataset…
Controlling Neural Style Transfer with Deep Reinforcement Learning
Chengming Feng, Jing Hu, Xin Wang +5
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters. In this paper, we propose t…
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
Jing Hu, Ziwei Luo, Chengming Feng +7
Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challen…
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation
Hao Chen, Peng Zheng, Xin Wang +5
As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent…
Attacking Important Pixels for Anchor-free Detectors
Yunxu Xie, Shu Hu, Xin Wang +4
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change the prediction result. Existing adversarial attacks o…