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Hierarchical multiples self-attention mechanism for multi-modal analysis.
- Source :
-
Multimedia Systems . Dec2023, Vol. 29 Issue 6, p3599-3608. 10p. - Publication Year :
- 2023
-
Abstract
- Because of the massive multimedia in daily life, people perceive the world by concurrently processing and fusing multi-modalities with high-dimensional data which may include text, vision, audio and some others. Depending on the popular Machine Learning, we would like to get much better fusion results. Therefore, multi-modal analysis has become an innovative field in data processing. By combining different modes, data can be more informative. However the difficulties of multi-modality analysis and processing lie in Feature extraction and Feature fusion. This paper focussed on this point to propose the BERT-HMAG model for feature extraction and LMF-SA model for multi-modality fusion. During the experiment, compared with traditional models, such as LSTM and Transformer, they are improved to a certain extent. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*FEATURE extraction
*ELECTRONIC data processing
Subjects
Details
- Language :
- English
- ISSN :
- 09424962
- Volume :
- 29
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Multimedia Systems
- Publication Type :
- Academic Journal
- Accession number :
- 173653682
- Full Text :
- https://doi.org/10.1007/s00530-023-01133-7