Search Results for author: Wenming Zheng

Found 24 papers, 2 papers with code

Speech Emotion Recognition via an Attentive Time-Frequency Neural Network

no code implementations22 Oct 2022 Cheng Lu, Wenming Zheng, Hailun Lian, Yuan Zong, Chuangao Tang, Sunan Li, Yan Zhao

The F-Encoder and T-Encoder model the correlations within frequency bands and time frames, respectively, and they are embedded into a time-frequency joint learning strategy to obtain the time-frequency patterns for speech emotions.

Speech Emotion Recognition

SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos

no code implementations18 Sep 2022 Xiaolin Xu, Yuan Zong, Wenming Zheng, Yang Li, Chuangao Tang, Xingxun Jiang, Haolin Jiang

In this paper, we present a large-scale, multi-source, and unconstrained database called SDFE-LV for spotting the onset and offset frames of a complete dynamic facial expression from long videos, which is known as the topic of dynamic facial expression spotting (DFES) and a vital prior step for lots of facial expression analysis tasks.

GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition

1 code implementation12 Apr 2022 Yang Li, Ji Chen, Fu Li, Boxun Fu, Hao Wu, Youshuo Ji, Yijin Zhou, Yi Niu, Guangming Shi, Wenming Zheng

GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.

Contrastive Learning EEG +2

Progressive Graph Convolution Network for EEG Emotion Recognition

no code implementations14 Dec 2021 Yijin Zhou, Fu Li, Yang Li, Youshuo Ji, Guangming Shi, Wenming Zheng, Lijian Zhang, Yuanfang Chen, Rui Cheng

Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion.

EEG EEG Emotion Recognition

Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

1 code implementation30 Nov 2021 Xingxun Jiang, Yuan Zong, Wenming Zheng, Jiateng Liu, Mengting Wei

To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i. e., the salient facial region selection.

Domain Adaptation Micro-Expression Recognition

Region attention and graph embedding network for occlusion objective class-based micro-expression recognition

no code implementations13 Jul 2021 Qirong Mao, Ling Zhou, Wenming Zheng, Xiuyan Shao, Xiaohua Huang

More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions.

Graph Embedding Micro-Expression Recognition

Hybrid Message Passing With Performance-Driven Structures for Facial Action Unit Detection

no code implementations CVPR 2021 Tengfei Song, Zijun Cui, Wenming Zheng, Qiang Ji

In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner.

Action Unit Detection Facial Action Unit Detection

Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation

no code implementations CVPR 2021 Tengfei Song, Zijun Cui, Yuru Wang, Wenming Zheng, Qiang Ji

Second, we introduce probabilistic graph convolution that allows to perform graph convolution on the distribution of Bayesian Network structure to extract AU structural features.

SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition

no code implementations19 Oct 2020 Jiateng Liu, Wenming Zheng, Yuan Zong

Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER).

Micro-Expression Recognition

A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition

no code implementations21 Sep 2020 Yang Li, Boxun Fu, Fu Li, Guangming Shi, Wenming Zheng

So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples.

EEG EEG Emotion Recognition

DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild

no code implementations13 Aug 2020 Xingxun Jiang, Yuan Zong, Wenming Zheng, Chuangao Tang, Wanchuang Xia, Cheng Lu, Jiateng Liu

Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild.

Facial Expression Recognition

Cross-Database Micro-Expression Recognition: A Benchmark

no code implementations19 Dec 2018 Yuan Zong, Tong Zhang, Wenming Zheng, Xiaopeng Hong, Chuangao Tang, Zhen Cui, Guoying Zhao

Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis.

Domain Adaptation Micro-Expression Recognition

Cross-database non-frontal facial expression recognition based on transductive deep transfer learning

no code implementations30 Nov 2018 Keyu Yan, Wenming Zheng, Tong Zhang, Yuan Zong, Zhen Cui

Cross-database non-frontal expression recognition is a very meaningful but rather difficult subject in the fields of computer vision and affect computing.

Facial Expression Recognition Transfer Learning

Context-Dependent Diffusion Network for Visual Relationship Detection

no code implementations11 Sep 2018 Zhen Cui, Chunyan Xu, Wenming Zheng, Jian Yang

Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images.

Object Recognition Visual Relationship Detection

Walk-Steered Convolution for Graph Classification

no code implementations16 Apr 2018 Jiatao Jiang, Chunyan Xu, Zhen Cui, Tong Zhang, Wenming Zheng, Jian Yang

As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters.

Classification General Classification +2

Tensor graph convolutional neural network

no code implementations27 Mar 2018 Tong Zhang, Wenming Zheng, Zhen Cui, Yang Li

For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs.

Matrix Completion

Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition

no code implementations27 Feb 2018 Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Jian Yang

To encode dynamic graphs, the constructed multi-scale local graph convolution filters, consisting of matrices of local receptive fields and signal mappings, are recursively performed on structured graph data of temporal and spatial domain.

Action Recognition Skeleton Based Action Recognition

Action-Attending Graphic Neural Network

no code implementations17 Nov 2017 Chaolong Li, Zhen Cui, Wenming Zheng, Chunyan Xu, Rongrong Ji, Jian Yang

The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision.

Action Analysis Action Recognition +1

Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition

no code implementations26 Jul 2017 Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, Guoying Zhao

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases.

Emotion Recognition Micro-Expression Recognition

Deep manifold-to-manifold transforming network for action recognition

no code implementations30 May 2017 Tong Zhang, Wenming Zheng, Zhen Cui, Chaolong Li

Symmetric positive definite (SPD) matrices (e. g., covariances, graph Laplacians, etc.)

Action Recognition

Spatial-Temporal Recurrent Neural Network for Emotion Recognition

no code implementations12 May 2017 Tong Zhang, Wenming Zheng, Zhen Cui, Yuan Zong, Yang Li

Then a bi-directional temporal RNN layer is further used to learn discriminative temporal dependencies from the sequences concatenating spatial features of each time slice produced from the spatial RNN layer.

EEG Emotion Recognition

Recurrent Regression for Face Recognition

no code implementations24 Jul 2016 Yang Li, Wenming Zheng, Zhen Cui

To address the sequential changes of images including poses, in this paper we propose a recurrent regression neural network(RRNN) framework to unify two classic tasks of cross-pose face recognition on still images and video-based face recognition.

Face Recognition regression

Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification

no code implementations NeurIPS 2009 Wenming Zheng, Zhouchen Lin

The method of common spatio-spectral patterns (CSSPs) is an extension of common spatial patterns (CSPs) by utilizing the technique of delay embedding to alleviate the adverse effects of noises and artifacts on the electroencephalogram (EEG) classification.

Classification EEG +1

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