Search Results for author: Mei Wang

Found 25 papers, 8 papers with code

Faceptor: A Generalist Model for Face Perception

3 code implementations14 Mar 2024 Lixiong Qin, Mei Wang, Xuannan Liu, Yuhang Zhang, Wei Deng, Xiaoshuai Song, Weiran Xu, Weihong Deng

This design enhances the unification of model structure while improving application efficiency in terms of storage overhead.

Age Estimation Attribute +3

Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis

no code implementations11 Mar 2024 Zijian Chen, Mei Wang, Weihong Deng, Hongzhi Shi, Dongchao Wen, Yingjie Zhang, Xingchen Cui, Jian Zhao

2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose.

Depth Estimation Face Recognition

Marginal Debiased Network for Fair Visual Recognition

no code implementations4 Jan 2024 Mei Wang, Weihong Deng, Sen Su

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 training data (bias-aligned samples), thus showing unfair behavior and arising controversy in the modern pluralistic and egalitarian society.

Fairness Meta-Learning

Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition

no code implementations1 Jan 2024 Ruizhuo Xu, Ke Wang, Chao Deng, Mei Wang, Xi Chen, Wenhui Huang, Junlan Feng, Weihong Deng

With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention.

Denoising Face Recognition

SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation

1 code implementation22 Aug 2023 Lixiong Qin, Mei Wang, Chao Deng, Ke Wang, Xi Chen, Jiani Hu, Weihong Deng

To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks.

Age Estimation Attribute +2

Adaptive Face Recognition Using Adversarial Information Network

no code implementations23 May 2023 Mei Wang, Weihong Deng

Supervision on pseudo-labeled samples attracts them towards their prototypes and would cause an intra-domain gap between pseudo-labeled samples and the remaining unlabeled samples within target domain, which results in the lack of discrimination in face recognition.

Domain Generalization Face Recognition +1

Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention

no code implementations5 Apr 2023 Linzhi Huang, Mei Wang, Jiahao Liang, Weihong Deng, Hongzhi Shi, Dongchao Wen, Yingjie Zhang, Jian Zhao

Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning.

Face Recognition

Cycle Label-Consistent Networks for Unsupervised Domain Adaptation

no code implementations27 May 2022 Mei Wang, Weihong Deng

The cycle label-consistent loss reinforces the consistency between ground-truth labels and pseudo-labels of source samples leading to statistically similar latent representations between source and target domains.

Unsupervised Domain Adaptation

Deep face recognition with clustering based domain adaptation

no code implementations27 May 2022 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 gathered from Internet are different from test images because of different lighting condition, pose and image quality.

Clustering Domain Adaptation +1

Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms

1 code implementation19 May 2022 Mei Wang, Weihong Deng

We introduce the Oracle-MNIST dataset, comprising of 28$\times $28 grayscale images of 30, 222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion.

Benchmarking BIG-bench Machine Learning

Unsupervised Structure-Texture Separation Network for Oracle Character Recognition

1 code implementation13 May 2022 Mei Wang, Weihong Deng, Cheng-Lin Liu

Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters.

Disentanglement Unsupervised Domain Adaptation

Meta Balanced Network for Fair Face Recognition

no code implementations13 May 2022 Mei Wang, Yaobin Zhang, Weihong Deng

Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones.

Face Recognition Meta-Learning

Augmented Geometric Distillation for Data-Free Incremental Person ReID

no code implementations CVPR 2022 Yichen Lu, Mei Wang, Weihong Deng

On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it.

Incremental Learning Person Re-Identification +1

Predicting Disease Progress with Imprecise Lab Test Results

no code implementations8 Jul 2021 Mei Wang, Jianwen Su, Zhihua Lin

In this method, each sample in imprecision range space has a certain probability to be the real value, participating in the loss calculation.

Impact of Medical Data Imprecision on Learning Results

no code implementations24 Jul 2020 Mei Wang, Jianwen Su, Haiqin Lu

In this paper, we initiate a study on the impact of imprecision on prediction results in a healthcare application where a pre-trained model is used to predict future state of hyperthyroidism for patients.

Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

no code implementations CVPR 2020 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.

Face Recognition Fairness +3

Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning

no code implementations25 Nov 2019 Mei Wang, Weihong Deng

To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses.

Face Recognition Fairness +3

Time-weighted Attentional Session-Aware Recommender System

no code implementations12 Sep 2019 Mei Wang, Weizhi Li, Yan Yan

Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information.

Collaborative Filtering Recommendation Systems

Deep Face Recognition: A Survey

7 code implementations18 Apr 2018 Mei Wang, Weihong Deng

Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction.

Face Recognition Face Verification +1

Deep Visual Domain Adaptation: A Survey

no code implementations10 Feb 2018 Mei Wang, Weihong Deng

Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data.

Domain Adaptation Face Recognition +4

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