604 citations · 752 across the 27 of their papers we have counts for
29 papers
Text-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis
Xiaohua Huang, Bingrui Lei, Xiuyan Shao +5
Multimodal sentiment analysis (MSA) aims to understand user-generated videos by integrating multiple modalities. Existing methods often treat modalities as symmetric, overlooking t…
Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings
Wei Shan, Tianyi Zhang, Yuan Zong +1
Personality assessment from asynchronous video interviews (AVIs) plays a crucial role in recruitment by enabling standardized and behaviorally grounded evaluation for job applicant…
AVI-Personality: A Trait-Activated Multimodal Dataset for Personality and Competency Assessment in Asynchronous Video Interviews
Tianyi Zhang, Jinwenxi Shang, Antonis Koutsoumpis +3
With the rapid development of AI-based personality and job-related competency assessment, Asynchronous Video Interviews (AVIs) are increasingly used in recruitment. However, existi…
LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion
Tianyi Zhang, Wei Shan, Yuan Zong +2
Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often…
Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions
Licai Sun, Xingxun Jiang, Haoyu Chen +7
Current facial emotion recognition systems are predominately trained to predict a fixed set of predefined categories or abstract dimensional values. This constrained form of superv…
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection
Yong Li, Menglin Liu, Zhen Cui +5
Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and…
Towards Realistic Emotional Voice Conversion using Controllable Emotional Intensity
Tianhua Qi, Shiyan Wang, Cheng Lu +3
Realistic emotional voice conversion (EVC) aims to enhance emotional diversity of converted audios, making the synthesized voices more authentic and natural. To this end, we propos…
Temporal Label Hierachical Network for Compound Emotion Recognition
Sunan Li, Hailun Lian, Cheng Lu +5
The emotion recognition has attracted more attention in recent decades. Although significant progress has been made in the recognition technology of the seven basic emotions, exist…
PAVITS: Exploring Prosody-aware VITS for End-to-End Emotional Voice Conversion
Tianhua Qi, Wenming Zheng, Cheng Lu +2
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 emotion…
Emotion-Aware Contrastive Adaptation Network for Source-Free Cross-Corpus Speech Emotion Recognition
Yan Zhao, Jincen Wang, Cheng Lu +4
Cross-corpus speech emotion recognition (SER) aims to transfer emotional knowledge from a labeled source corpus to an unlabeled corpus. However, prior methods require access to sou…
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition
Yong Wang, Cheng Lu, Hailun Lian +4
Swin-Transformer has demonstrated remarkable success in computer vision by leveraging its hierarchical feature representation based on Transformer. In speech signals, emotional inf…
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation
Cheng Lu, Yuan Zong, Hailun Lian +3
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 featur…
Towards Domain-Specific Cross-Corpus Speech Emotion Recognition Approach
Yan Zhao, Yuan Zong, Hailun Lian +3
Cross-corpus speech emotion recognition (SER) poses a challenge due to feature distribution mismatch, potentially degrading the performance of established SER methods. In this pape…
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition
Jie Zhu, Yuan Zong, Jingang Shi +3
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 re…
Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
Yan Zhao, Yuan Zong, Jincen Wang +4
In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-c…
Time-Frequency Transformer: A Novel Time Frequency Joint Learning Method for Speech Emotion Recognition
Yong Wang, Cheng Lu, Yuan Zong +3
In this paper, we propose a novel time-frequency joint learning method for speech emotion recognition, called Time-Frequency Transformer. Its advantage is that the Time-Frequency T…
Learning Local to Global Feature Aggregation for Speech Emotion Recognition
Cheng Lu, Hailun Lian, Wenming Zheng +3
Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion corr…
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition
Yan Zhao, Jincen Wang, Yuan Zong +3
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 (S…
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network
Cheng Lu, Wenming Zheng, Hailun Lian +4
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).…
SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos
Xiaolin Xu, Yuan Zong, Wenming Zheng +4
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 fr…
Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition
Xingxun Jiang, Yuan Zong, Wenming Zheng +2
Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize t…
SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition
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…
Spatial Transformer Point Convolution
Yuan Fang, Chunyan Xu, Zhen Cui +2
Point clouds are unstructured and unordered in the embedded 3D space. In order to produce consistent responses under different permutation layouts, most existing methods aggregate…
DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild
Xingxun Jiang, Yuan Zong, Wenming Zheng +4
Recently, facial expression recognition (FER) in the wild has gained a lot of researchers' attention because it is a valuable topic to enable the FER techniques to move from the la…