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20182022
most citedFrequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

21 citations · 46 across the 5 of their papers we have counts for

collaborators

11 papers

eess.IV20222 cited

A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information

Jing Fang, Yinbo Yu, Zhongyuan Wang +2

Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Spatial-domain information has been widely exploited to impleme…

cs.CV202213 cited

Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing

Zhuo Wang, Zezheng Wang, Zitong Yu +4

With diverse presentation attacks emerging continually, generalizable face anti-spoofing (FAS) has drawn growing attention. Most existing methods implement domain generalization (D…

cs.CV20219 cited

TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network

Yuanzhi Wang, Tao Lu, Yanduo Zhang +4

Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps, faci…

eess.IV2021

Omniscient Video Super-Resolution

Peng Yi, Zhongyuan Wang, Kui Jiang +4

Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previousl…

cs.CV202121 cited

Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

Jiaming Li, Hongtao Xie, Jiahong Li +2

Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound a…

cs.CV20211 cited

When Face Recognition Meets Occlusion: A New Benchmark

Baojin Huang, Zhongyuan Wang, Guangcheng Wang +5

The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, weari…