.png){kind=icon}
返回
Multi-modal data clustering using deep learning: A systematic review
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2024.128348.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-modal clustering represents a formidable challenge in the domain of unsupervised learning. The objective of multi-modal clustering is to categorize data collected from diverse modalities, such as audio, visual, and textual sources, into distinct clusters. These clustering techniques operate by extracting shared features across modalities in an unsupervised manner, where the identified common features exhibit high correlations within real-world objects. Recognizing the importance of perceiving the correlated nature of these features is vital for enhancing clustering accuracy in multi-modal settings. This survey explores Deep Learning (DL) techniques applied to multi-modal clustering, encompassing methodologies such as Convolutional Neural Networks (CNN), Autoencoders (AE), Recurrent Neural Networks (RNN), and Graph Convolutional Networks (GCN). Notably, this survey represents the first attempt to investigate DL techniques specifically for multi-modal clustering. The survey presents a novel taxonomy for DL-based multi-modal clustering, conducts a comparative analysis of various multi-modal clustering approaches, and deliberates on the datasets employed in the evaluation process. Additionally, the survey identifies research gaps within the realm of multi-modal clustering, offering insights into potential future avenues for research.
Keyword:
Multi-modal data
Clustering algorithms
Deep learning
Review article
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Graph Embedding Contrastive Multi-Modal Representation Learning for Clustering面向聚类的图嵌入对比多模态表示学习
PREAI
分享收藏Vertical and Diurnal Characterization of Volatile Organic Compounds in Ambient Air in Urban Areas
OAAI
OAAI分享收藏DEEP-AD: A Multimodal Temporal Video Segmentation Framework for Online Video Advertising.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects多模态数据融合: 方法、挑战与展望综述PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏
查看更多内容