activity
20182022
most citedEnhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture

27 citations · 60 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV20215 cited

Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning

Shiyu Tang, Peijun Tang, Yanxiang Gong +2

Previous feature alignment methods in Unsupervised domain adaptation(UDA) mostly only align global features without considering the mismatch between class-wise features. In this wo…

eess.IV20204 cited

Single Image Super-Resolution via Residual Neuron Attention Networks

Wenjie Ai, Xiaoguang Tu, Shilei Cheng +1

Deep Convolutional Neural Networks (DCNNs) have achieved impressive performance in Single Image Super-Resolution (SISR). To further improve the performance, existing CNN-based meth…

cs.CV2020

What's the relationship between CNNs and communication systems?

Hao Ge, Xiaoguang Tu, Yanxiang Gong +2

The interpretability of Convolutional Neural Networks (CNNs) is an important topic in the field of computer vision. In recent years, works in this field generally adopt a mature mo…

cs.CV20191 cited

Defending from adversarial examples with a two-stream architecture

Hao Ge, Xiaoguang Tu, Mei Xie +1

In recent years, deep learning has shown impressive performance on many tasks. However, recent researches showed that deep learning systems are vulnerable to small, specially craft…

cs.CV20191 cited

STELA: A Real-Time Scene Text Detector with Learned Anchor

Linjie Deng, Yanxiang Gong, Xinchen Lu +3

To achieve high coverage of target boxes, a normal strategy of conventional one-stage anchor-based detectors is to utilize multiple priors at each spatial position, especially in s…

cs.CV2019

Learning Robust 3D Face Reconstruction and Discriminative Identity Representation

Yao Luo, Xiaoguang Tu, Mei Xie

3D face reconstruction from a single 2D image is a very important topic in computer vision. However, the current reconstruction methods are usually non-sensitive to face identities…