30 citations · 91 across the 16 of their papers we have counts for
5 papers · 1 filter
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
Zhijin Ge, Fanhua Shang, Hongying Liu +4
Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high suc…
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Zhijin Ge, Hongying Liu, Xiaosen Wang +2
Transfer-based attack adopts the adversarial examples generated on the surrogate model to attack various models, making it applicable in the physical world and attracting increasin…
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling
Hongying Liu, Peng Zhao, Zhubo Ruan +2
Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very impo…
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
Hongying Liu, Zhubo Ruan, Chaowei Fang +4
Spherical videos, also known as \ang{360} (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays. They attract large amou…
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
Hongying Liu, Zhubo Ruan, Peng Zhao +5
In recent years, deep learning has made great progress in many fields such as image recognition, natural language processing, speech recognition and video super-resolution. In this…