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。”
no code implementations • 1 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).
no code implementations • 3 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.
no code implementations • 19 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.
no code implementations • 18 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.
no code implementations • 6 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.
no code implementations • 7 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).
no code implementations • 17 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.
no code implementations • 22 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.
no code implementations • 18 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.
1 code implementation • 30 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.
no code implementations • 19 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).
no code implementations • 3 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.
no code implementations • 13 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.
Ranked #17 on Dynamic Facial Expression Recognition on DFEW
Dynamic Facial Expression Recognition Facial Expression Recognition +1
no code implementations • 19 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.
no code implementations • 30 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
no code implementations • 26 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.
no code implementations • 12 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.