9 citations · 17 across the 8 of their papers we have counts for
8 papers
Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis
Huisheng Mao, Baozheng Zhang, Hua Xu +2
Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among…
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences
Kaicheng Yang, Ruxuan Zhang, Hua Xu +1
Inter-modal interaction plays an indispensable role in multimodal sentiment analysis. Due to different modalities sequences are usually non-alignment, how to integrate relevant inf…
Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module
Yihe Liu, Ziqi Yuan, Huisheng Mao +7
Multimodal sentiment analysis (MSA), which supposes to improve text-based sentiment analysis with associated acoustic and visual modalities, is an emerging research area due to its…
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
Zhijing Wu, Hua Xu, Jingliang Fang +1
Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the develo…
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
Huisheng Mao, Ziqi Yuan, Hua Xu +3
M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive dem…
Consistent Representation Learning for Continual Relation Extraction
Kang Zhao, Hua Xu, Jiangong Yang +1
Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones. Some previous work has proved that storing a f…
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis
Wenmeng Yu, Hua Xu, Ziqi Yuan +1
Representation Learning is a significant and challenging task in multimodal learning. Effective modality representations should contain two parts of characteristics: the consistenc…
Learning Robust Heterogeneous Signal Features from Parallel Neural Network for Audio Sentiment Analysis
Feiyang Chen, Ziqian Luo
Audio Sentiment Analysis is a popular research area which extends the conventional text-based sentiment analysis to depend on the effectiveness of acoustic features extracted from…