TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection
In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people’s lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9961146/ |
| _version_ | 1827926987999543296 |
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| author | Li Zhou Zhenyu Liu Zixuan Shangguan Xiaoyan Yuan Yutong Li Bin Hu |
| author_facet | Li Zhou Zhenyu Liu Zixuan Shangguan Xiaoyan Yuan Yutong Li Bin Hu |
| author_sort | Li Zhou |
| collection | DOAJ |
| description | In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people’s lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attention-based multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model. |
| first_indexed | 2024-03-13T05:45:35Z |
| format | Article |
| id | doaj.art-38795515a41648ce848fe7a956cb942d |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:45:35Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-38795515a41648ce848fe7a956cb942d2023-06-13T20:09:48ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-013166967910.1109/TNSRE.2022.32241359961146TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression DetectionLi Zhou0https://orcid.org/0000-0001-7734-2699Zhenyu Liu1https://orcid.org/0000-0001-8401-9056Zixuan Shangguan2Xiaoyan Yuan3Yutong Li4Bin Hu5https://orcid.org/0000-0003-3514-5413Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, ChinaGansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou, ChinaIn recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people’s lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attention-based multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model.https://ieeexplore.ieee.org/document/9961146/Depressionvlognon-verbal behaviorsautomatic detectiontime-aware attention-based multimodal fusion depression detection network (TAMFN) |
| spellingShingle | Li Zhou Zhenyu Liu Zixuan Shangguan Xiaoyan Yuan Yutong Li Bin Hu TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection IEEE Transactions on Neural Systems and Rehabilitation Engineering Depression vlog non-verbal behaviors automatic detection time-aware attention-based multimodal fusion depression detection network (TAMFN) |
| title | TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection |
| title_full | TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection |
| title_fullStr | TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection |
| title_full_unstemmed | TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection |
| title_short | TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection |
| title_sort | tamfn time aware attention multimodal fusion network for depression detection |
| topic | Depression vlog non-verbal behaviors automatic detection time-aware attention-based multimodal fusion depression detection network (TAMFN) |
| url | https://ieeexplore.ieee.org/document/9961146/ |
| work_keys_str_mv | AT lizhou tamfntimeawareattentionmultimodalfusionnetworkfordepressiondetection AT zhenyuliu tamfntimeawareattentionmultimodalfusionnetworkfordepressiondetection AT zixuanshangguan tamfntimeawareattentionmultimodalfusionnetworkfordepressiondetection AT xiaoyanyuan tamfntimeawareattentionmultimodalfusionnetworkfordepressiondetection AT yutongli tamfntimeawareattentionmultimodalfusionnetworkfordepressiondetection AT binhu tamfntimeawareattentionmultimodalfusionnetworkfordepressiondetection |