2.2k citations · 3.4k across the 14 of their papers we have counts for
14 papers
Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition
Mei Wang, Weihong Deng, Jiani Hu +1
The study of oracle characters plays an important role in Chinese archaeology and philology. However, the difficulty of collecting and annotating real-world scanned oracle characte…
Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis
Zijian Chen, Mei Wang, Weihong Deng +5
2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve…
Faceptor: A Generalist Model for Face Perception
Lixiong Qin, Mei Wang, Xuannan Liu +5
With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing m…
Marginal Debiased Network for Fair Visual Recognition
Mei Wang, Weihong Deng, Jiani Hu +1
Deep neural networks (DNNs) are often prone to learn the spurious correlations between target classes and bias attributes, like gender and race, inherent in a major portion of trai…
Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence
Ruizhuo Xu, Linzhi Huang, Mei Wang +2
Self-supervised pre-training paradigms have been extensively explored in the field of skeleton-based action recognition. In particular, methods based on masked prediction have push…
Oracle Character Recognition using Unsupervised Discriminative Consistency Network
Mei Wang, Weihong Deng, Sen Su
Ancient history relies on the study of ancient characters. However, real-world scanned oracle characters are difficult to collect and annotate, posing a major obstacle for oracle c…
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
Wenjie Li, Mei Wang, Kai Zhang +6
Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep…
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Linzhi Huang, Mei Wang, Jiahao Liang +5
Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work f…
Deep face recognition with clustering based domain adaptation
Mei Wang, Weihong Deng
Despite great progress in face recognition tasks achieved by deep convolution neural networks (CNNs), these models often face challenges in real world tasks where training images g…
Meta Balanced Network for Fair Face Recognition
Mei Wang, Yaobin Zhang, Weihong Deng
Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment in…
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning
Mei Wang, Weihong Deng
Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts…
Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Mei Wang, Weihong Deng, Jiani Hu +2
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition. In this paper, we first contribute a dedicated dataset called Racial F…
Deep Face Recognition: A Survey
Mei Wang, Weihong Deng
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research land…
Deep Visual Domain Adaptation: A Survey
Mei Wang, Weihong Deng
Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature su…