中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation

文献类型：期刊论文

作者Lian, Zheng3; Chen, Lan3; Sun, Licai1; Liu, Bin3; Tao, Jianhua1,2
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2023-07-01
卷号45期号:7页码:8419-8432
ISSN号0162-8828
关键词Oral communication Correlation Data models Task analysis Feature extraction Tensors Benchmark testing Conversational data graph complete network (GCNet) incomplete multimodal learning speaker-sensitive modeling temporal-sensitive modeling
DOI10.1109/TPAMI.2023.3234553
通讯作者Liu, Bin(liubin@nlpr.ia.ac.cn) ; Tao, Jianhua(jhtao@tsinghua.edu.cn)
英文摘要Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content and other aspects also attracts increasing attention from researchers due to its widespread application in human-computer interaction. In real-world environments, we often encounter the problem of incomplete modalities, which has become a core issue of conversation understanding. To address this problem, researchers propose various methods. However, existing approaches are mainly designed for individual utterances rather than conversational data, which cannot fully exploit temporal and speaker information in conversations. To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)," filling the gap of existing works. Our GCNet contains two well-designed graph neural network-based modules, "Speaker GNN" and "Temporal GNN," to capture temporal and speaker dependencies. To make full use of complete and incomplete data, we jointly optimize classification and reconstruction tasks in an end-to-end manner. To verify the effectiveness of our method, we conduct experiments on three benchmark conversational datasets. Experimental results demonstrate that our GCNet is superior to existing state-of-the-art approaches in incomplete multimodal learning.
资助项目National Key Research and Development Plan of China[2020AAA0140003] ; National Natural Science Foundation of China (NSFC)[62201572] ; National Natural Science Foundation of China (NSFC)[61831022] ; National Natural Science Foundation of China (NSFC)[62276259] ; National Natural Science Foundation of China (NSFC)[U21B2010] ; Open Research Projects of Zhejiang Lab[2021KH0AB06]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:001004665900031
资助机构National Key Research and Development Plan of China ; National Natural Science Foundation of China (NSFC) ; Open Research Projects of Zhejiang Lab
源URL[http://ir.ia.ac.cn/handle/173211/53615]  
专题多模态人工智能系统全国重点实验室
通讯作者Liu, Bin; Tao, Jianhua
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Lian, Zheng,Chen, Lan,Sun, Licai,et al. GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(7):8419-8432.
APA Lian, Zheng,Chen, Lan,Sun, Licai,Liu, Bin,&Tao, Jianhua.(2023).GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(7),8419-8432.
MLA Lian, Zheng,et al."GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.7(2023):8419-8432.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。