Wenming Zheng

Biographic Data

ID6222905
NAMEWenming Zheng
GIVEN NAMESWenming
FAMILY NAMEZheng
SIGNATUREZHENG W
AFFILIATIONSSoutheast University
ORCID0000-0002-7764-5179
VERIFIEDYes
TOTAL WORKS16
TOTAL CITATIONS0
WORKS AS AUTHOR16
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2013
LATEST PUBLICATION YEAR2026
H-INDEX0

  • A Survey on Deep Learning for Group-Level Emotion Recognition

    Open AccessXiaohua Huang, Xiaopeng Hong et al.ARTICLEIEEE Transactions on Computational…2026

    With the rapid advancement of artificial intelligence, group-level emotion recognition (GER) has emerged as an important domain in human behavior analysis. Early GER methods primarily relied on handcrafted features. However, the recent success of deep learning has shifted the focus toward neural network-based solution, enabling more effective exploitation of the rich visual and contextual cues in group images and videos. Unlike individual-level e…

  • Trend-Aware Multiscale Spatial-Temporal Graph Convolution Network for P300 Detection

    Open AccessJincen Wang, Wenming Zheng et al.ARTICLEIEEE Transactions on Computational…2026

    P300-based brain–computer interfaces (BCIs) enable direct communication between the brain and external devices by decoding P300 potentials, playing a vital role in rehabilitation and cognitive neuroscience research. Accurate detection of P300 potentials is essential for the successful implementation of P300-based BCIs. Current mainstream P300 detection algorithms are based on multichannel electroencephalogram (EEG) signals, and while promising de…

  • Dynamic Graph Consistent Weighted Subspace Learning for Cross-Domain Speech Emotion Recognition

    Open AccessYifan Wang, Peng Song et al.ARTICLEIEEE Transactions on Computational…2026

    In recent years, cross-domain speech emotion recognition (SER) has attracted considerable interest. Most transfer subspace learning based SER methods lack unified adaptive constraints, making it difficult to balance discriminative capability and domain alignment, which limits their cross-domain generalization. To address these problems, we propose a novel domain adaptation (DA) approach called dynamic graph consistent weighted subspace learning (…

  • Dual-Hypergraph Based Symmetrical Self-Representation Learning for Cross-Domain Facial Expression Recognition

    Open AccessYuhan Cheng, Peng Song et al.ARTICLEIEEE Transactions on Computational…2026

  • Coupled Sparse Subspace Alignment-Based Domain Adaptation for Speech Emotion Recognition

    Open AccessSiqi Fu, Peng Song et al.ARTICLEIEEE Transactions on Computational…2026

    Speech emotion recognition (SER) is crucial for human–computer interaction (HCI), yet remains a challenge in cross-domain scenarios. Emotional expressions vary significantly across speakers, languages, and recording conditions, leading to serious domain shifts. Coupled subspace learning has recently attracted considerable attention in domain adaptation (DA) as an effective approach to mitigating domain discrepancies by capturing common and domain…

  • Common Discriminative Latent Space Learning for Cross-Domain Speech Emotion Recognition

    Open AccessSiqi Fu, Peng Song et al.ARTICLEIEEE Transactions on Computational…2025

    Cross-domain speech emotion recognition (SER) has received increasing attention in recent years. Existing transfer subspace learning and regression-based SER methods have the following drawbacks. The features in the subspace are still insufficiently representative and discriminative, and direct regression would lead to information loss. To address these problems, we present a novel common discriminative latent space learning (CDLSL) method for cr…

  • Towards Domain-Specific Cross-Corpus Speech Emotion Recognition Approach

    Open AccessYan Zhao, Yuan Zong et al.ARTICLEIEEE Transactions on Computational…2025

    Cross-corpus speech emotion recognition (SER) poses a challenge due to feature distribution mismatch between the training and testing speech samples, potentially degrading the performance of established SER methods. In this article, we tackle this challenge by proposing a novel transfer subspace learning method called acoustic knowledge-guided transfer linear regression (AKTLR). Unlike existing approaches, which often overlook domain-specific kno…

  • Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

    Open AccessYan Zhao, Yuan Zong et al.ARTICLEIEEE Transactions on Computational…2024

    In this article, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDANs) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDANs), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This t…

  • Common Latent Embedding Space for Cross-Domain Facial Expression Recognition

    Open AccessRun Wang, Peng Song et al.ARTICLEIEEE Transactions on Computational…2024

    In practical facial expression recognition (FER), the training data and test data are often obtained from different domains. It is obvious that the domain disparity could significantly degrade the recognition performance. To tackle this challenging cross-domain FER problem, we put forward a novel method termed common latent embedding space (CLES). To be specific, first, we obtain a common embedding space for cross-domain samples by matrix factori…

  • Graph-Diffusion-Based Domain-Invariant Representation Learning for Cross-Domain Facial Expression Recognition

    Open AccessRun Wang, Peng Song et al.ARTICLEIEEE Transactions on Computational…2024

    The precondition that most of the existing facial expression recognition (FER) algorithms have succeeded lies in that the training (source) and test (target) samples are independent of each other and identically distributed. However, it is too strict to satisfy this precondition in the real-world. To this end, we propose a novel graph-diffusion-based domain-invariant representation learning (GDRL) model for the cross-domain FER scenario where the…

  • Adaptive Dual-Space Network With Multigraph Fusion for EEG-Based Emotion Recognition

    Open AccessMengqing Ye, C L Philip Chen et al.ARTICLEIEEE Transactions on Computational…2024

    Most of the work on electroencephalogram (EEG)-based emotion recognition aims to extract the distinguishing features from high-dimensional EEG signals, ignoring the complementarity of information between EEG latent space and graph space. Furthermore, the influence of brain connectivity on emotions encompasses both physical structure and functional connectivity, which may have varying degrees of importance for different individuals. To address the…

  • Speech Emotion Recognition via an Attentive Time–Frequency Neural Network

    Open AccessCheng Lu, Wenming Zheng et al.ARTICLEIEEE Transactions on Computational…2023

    Spectrogram is commonly used as the input feature of deep neural networks to learn the high(er)-level time–frequency pattern of speech signal for speech emotion recognition (SER). Generally, different emotions correspond to specific energy activations both within frequency bands and time frames on spectrogram, which indicates the frequency and time domains are both essential to represent the emotion for SER. However, recent spectrogram-based work…

  • Towards a computer-assisted comprehensive evaluation of visual motor integration for children with autism spectrum disorder

    Rujing Zhang, Jingying Chen et al.ARTICLEInteractive Learning Environments2023

    Visual motor integration (VMI) is considered a key predictor of adaptive functioning in children with autism spectrum disorder (ASD). However, information regarding the VMI characteristics of children with ASD is scarce because existing assessment methods have focused on outcome measures while neglecting process measures due to the high failure rates in children with ASD. Therefore, the current pilot study was conducted to explore the appropriate…

  • Depression Assessment Method

    Open AccessHongli Chang, Yuan Zong et al.ARTICLEFrontiers in Psychiatry2022

    The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-cha…

  • Links between Marital Relationship and Child-Parent Relationship

    Zongbao Liang, Guangzhen Zhang et al.ARTICLEActa Psychologica Sinica2013

    摘要

  • A Multilevel Analysis of the Developmental Trajectory of Preschoolers’ Effortful Control and Prediction by Parental Parenting Style

    Zongbao Liang, Guangzhen Zhang et al.ARTICLEActa Psychologica Sinica2013

    摘要: 通过3年追踪研究, 采用问卷法考察了474名学前儿童努力控制发展变化的轨迹, 以及初始父母养育方式对其发展水平和发展速率的影响。多水平分析的结果表明:(1)学前儿童的努力控制呈线性增长, 其发展水平和增长速度均存在明显的个体差异, 努力控制初始水平较高的儿童, 其增长速度也较快。(2)初始的父母鼓励与接纳对儿童努力控制的最后发展水平有促进作用, 而父母的拒绝与惩罚则起反作用。(3)父亲鼓励与接纳能加速儿童努力控制的平均增长速率, 而母亲鼓励与接纳则会延缓其平均增长速率。(4)初始的父亲养育方式可分别解释儿童努力控制发展水平和发展速率10%和2%的变异, 初始的母亲养育方式可分别解释其14%和3%的变异, 父母养育方式累计可分别解释其19%和10%的变异