Deep Multi-Source Visual Fusion With Transformer Model for Video Content Filtering [* Cross-Reference *]

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Senthil Murugan Nagarajan; Ganesh Gopal Devarajan; Asha Jerlin M; Daniel Arockiam; Ali Kashif Bashir; Maryam M. Al Dabel, 2025, "Deep Multi-Source Visual Fusion With Transformer Model for Video Content Filtering [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.3UQ9IF, LIST Open Repository, V1

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Description	As YouTube content continues to grow, advanced filtering systems are crucial to ensuring a safe and enjoyable user experience. We present MFusTSVD, a multi-modal model for classifying YouTube video content by analyzing text, audio, and video images. MFusTSVD uses specialized methods to extract features from audio and video images, while processing text data with BERT Transformers. Our key innovation includes two new BERT-based multi-modal fusion methods: B-SMTLMF and B-CMTLRMF. These methods combine features from different data types and improve the model's ability to understand each type of data, including detailed audio patterns, leading to better content classification and speech-related separation. MFusTSVD is designed to perform better than existing models in terms of accuracy, precision, recall, and F-measure. Tests show that MFusTSVD consistently outperforms popular models like Memory Fusion Network, Early Fusion LSTM, Late Fusion LSTM, and multi-modal Transformer across different content types and evaluation measures. In particular, MFusTSVD effectively balances precision and recall, which makes it especially useful for identifying inappropriate speech and audio content, as well as broader categories, ensuring reliable and robust content moderation. (2025-05-01) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1109/jstsp.2025.3569446 for the original and latest version of the publication* (2026-07-01)
Subject	Computer and Information Science; Physics
Keyword	Computer science, Artificial intelligence, Computer vision, Transformer, Fusion, Sensor fusion, Content (measure theory), Pattern recognition (psychology), Speech recognition
License/Data Use Agreement	Custom Dataset Terms


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Citation Metadata

Persistent Identifier	perma:LIST.3UQ9IF
Publication Date	2026-07-06
Title	Deep Multi-Source Visual Fusion With Transformer Model for Video Content Filtering [* Cross-Reference *]
Other Identifier	https://doi.org/10.1109/jstsp.2025.3569446 OpenAlex ID: https://openalex.org/W4410294745
Author	Senthil Murugan Nagarajan (University of Luxembourg, Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0001-9284-7724 Ganesh Gopal Devarajan (Amity University) - ORCID: https://orcid.org/0000-0003-0036-7841 Asha Jerlin M (Vellore Institute of Technology University) Daniel Arockiam - ORCID: https://orcid.org/0000-0001-5564-2332 Ali Kashif Bashir (Manchester Metropolitan University) - ORCID: https://orcid.org/0000-0003-2601-9327 Maryam M. Al Dabel (University of Hafr Al-Batin) - ORCID: https://orcid.org/0000-0003-4371-8939
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	As YouTube content continues to grow, advanced filtering systems are crucial to ensuring a safe and enjoyable user experience. We present MFusTSVD, a multi-modal model for classifying YouTube video content by analyzing text, audio, and video images. MFusTSVD uses specialized methods to extract features from audio and video images, while processing text data with BERT Transformers. Our key innovation includes two new BERT-based multi-modal fusion methods: B-SMTLMF and B-CMTLRMF. These methods combine features from different data types and improve the model's ability to understand each type of data, including detailed audio patterns, leading to better content classification and speech-related separation. MFusTSVD is designed to perform better than existing models in terms of accuracy, precision, recall, and F-measure. Tests show that MFusTSVD consistently outperforms popular models like Memory Fusion Network, Early Fusion LSTM, Late Fusion LSTM, and multi-modal Transformer across different content types and evaluation measures. In particular, MFusTSVD effectively balances precision and recall, which makes it especially useful for identifying inappropriate speech and audio content, as well as broader categories, ensuring reliable and robust content moderation. (2025-05-01) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.1109/jstsp.2025.3569446 for the original and latest version of the publication* (2026-07-01)
Subject	Computer and Information Science; Physics
Keyword	Computer science Artificial intelligence Computer vision Transformer Fusion Sensor fusion Content (measure theory) Pattern recognition (psychology) Speech recognition
Topic Classification	Image Retrieval and Classification Techniques Advanced Image and Video Retrieval Techniques Video Analysis and Summarization
Deposit Date	2025-05-01
Data Type	Article
Data Source	IEEE Journal of Selected Topics in Signal Processing

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