activity
20222025
most citedVulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks

3 citations · 5 across the 11 of their papers we have counts for

collaborators

11 papers

eess.SP2025

Restoration of contaminated data in an Intensity Mapping survey using deep neural networks

Lin-Cheng Li, Jia-Yu Lin, Yuan-Gen Wang +1

21-cm Intensity Mapping (IM) is a promising approach to detecting information about the large-scale structure beyond the local universe. One of the biggest challenges for an IM obs…

cs.CV20241 cited

Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception

Jiancong Feng, Yuan-Gen Wang, Mingjie Li +1

Self-similarity techniques are booming in blind super-resolution (SR) due to accurate estimation of the degradation types involved in low-resolution images. However, high-dimension…

cs.CV2024

CLIPVQA:Video Quality Assessment via CLIP

Fengchuang Xing, Mingjie Li, Yuan-Gen Wang +2

In learning vision-language representations from web-scale data, the contrastive language-image pre-training (CLIP) mechanism has demonstrated a remarkable performance in many visi…

cs.CV2024

-norm Distortion-Efficient Adversarial Attack

Chao Zhou, Yuan-Gen Wang, Zi-jia Wang +1

Adversarial examples have shown a powerful ability to make a well-trained model misclassified. Current mainstream adversarial attack methods only consider one of the distortions am…

cs.CV2024

Query-Efficient Hard-Label Black-Box Attack against Vision Transformers

Chao Zhou, Xiaowen Shi, Yuan-Gen Wang

Recent studies have revealed that vision transformers (ViTs) face similar security risks from adversarial attacks as deep convolutional neural networks (CNNs). However, directly ap…

cs.CV2024

Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment

Tianwei Zhou, Songbai Tan, Wei Zhou +3

With the increasing maturity of the text-to-image and image-to-image generative models, AI-generated images (AGIs) have shown great application potential in advertisement, entertai…