Search Results for author: Yuan Zong

Found 19 papers, 1 papers with code

基于双编码器的医学文本中文分词(Chinese word segmentation of medical text based on dual-encoder)

no code implementations CCL 2021 Yuan Zong, Baobao Chang

“中文分词是自然语言处理领域的基础工作, 然而前人的医学文本分词工作都只是直接套用通用分词的方法, 而医学文本多专用术语的特点让分词系统需要对医学专用术语和医学文本中的非医学术语文本提供不同的分词粒度。本文提出了双编码器医学文本中文分词模型, 利用辅助编码器为医学专有术语提供粗粒度表示。模型将需要粗粒度分词的医学专用术语和需要通用分词粒度的文本分开, 在提升医学专用术语的分词能力的同时最大限度地避免了其粗粒度对于医学文本中通用文本分词的干扰。”

Chinese Word Segmentation

EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model

no code implementations1 May 2024 Deng Li, Xin Liu, Bohao Xing, Baiqiang Xia, Yuan Zong, Bihan Wen, Heikki Kälviäinen

In contrast, long sequential videos can reveal authentic emotions; 2) Previous studies commonly utilize various signals such as facial, speech, and even sensitive biological signals (e. g., electrocardiogram).

De-identification Emotion Recognition +2

PAVITS: Exploring Prosody-aware VITS for End-to-End Emotional Voice Conversion

no code implementations3 Mar 2024 Tianhua Qi, Wenming Zheng, Cheng Lu, Yuan Zong, Hailun Lian

In this paper, we propose Prosody-aware VITS (PAVITS) for emotional voice conversion (EVC), aiming to achieve two major objectives of EVC: high content naturalness and high emotional naturalness, which are crucial for meeting the demands of human perception.

Voice Conversion

Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition

no code implementations19 Jan 2024 Yong Wang, Cheng Lu, Hailun Lian, Yan Zhao, Björn Schuller, Yuan Zong, Wenming Zheng

These segment-level patches are then encoded using a stack of Swin blocks, in which a local window Transformer is utilized to explore local inter-frame emotional information across frame patches of each segment patch.

Speech Emotion Recognition

Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation

no code implementations18 Jan 2024 Cheng Lu, Yuan Zong, Hailun Lian, Yan Zhao, Björn Schuller, Wenming Zheng

In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers.

Domain Adaptation Speech Emotion Recognition

PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions

no code implementations6 Nov 2023 Liu Liu, Guang Li, Dingfan Deng, Jinhua Yu, Yuan Zong

In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions.

Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition

no code implementations7 Oct 2023 Jie Zhu, Yuan Zong, Jingang Shi, Cheng Lu, Hongli Chang, Wenming Zheng

This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O).

Learning-To-Rank Micro Expression Recognition +1

Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

no code implementations17 Feb 2023 Yan Zhao, Jincen Wang, Yuan Zong, Wenming Zheng, Hailun Lian, Li Zhao

In this paper, we propose a novel deep transfer learning method called deep implicit distribution alignment networks (DIDAN) to deal with cross-corpus speech emotion recognition (SER) problem, in which the labeled training (source) and unlabeled testing (target) speech signals come from different corpora.

Cross-corpus Speech Emotion Recognition +1

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.

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 +2

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 Micro-Expression Recognition

Spatial Transformer Point Convolution

no code implementations3 Sep 2020 Yuan Fang, Chunyan Xu, Zhen Cui, Yuan Zong, Jian Yang

In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.

Dictionary Learning Semantic Segmentation

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.

Dynamic Facial Expression Recognition Facial Expression Recognition +1

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 +1

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 Facial Expression Recognition (FER) +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 +1

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

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