4 citations · 10 across the 7 of their papers we have counts for
7 papers
Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior
Yi Yu, Yufei Wang, Wenhan Yang +5
Recent advancements in deep learning-based compression techniques have surpassed traditional methods. However, deep neural networks remain vulnerable to backdoor attacks, where pre…
Transferable Adversarial Attacks on SAM and Its Downstream Models
Song Xia, Wenhan Yang, Yi Yu +4
The utilization of large foundational models has a dilemma: while fine-tuning downstream tasks from them holds promise for making use of the well-generalized knowledge in practical…
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
Laniqng Guo, Chong Wang, Yufei Wang +5
Shadow removal aims at restoring the image content within shadow regions, pursuing a uniform distribution of illumination that is consistent between shadow and non-shadow regions.…
ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model
Yufei Wang, Zhihao Li, Lanqing Guo +3
Recently, 3D Gaussian Splatting (3DGS) has become a promising framework for novel view synthesis, offering fast rendering speeds and high fidelity. However, the large number of Gau…
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
Yi Yu, Yufei Wang, Song Xia +4
Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks c…
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild
Honghao Fu, Yufei Wang, Wenhan Yang +2
Blind image quality assessment (IQA) in the wild, which assesses the quality of images with complex authentic distortions and no reference images, presents significant challenges.…
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Yufei Wang, Wenhan Yang, Xinyuan Chen +7
While super-resolution (SR) methods based on diffusion models exhibit promising results, their practical application is hindered by the substantial number of required inference ste…