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- W4312758889 abstract "Attention-based networks currently identify their effectiveness in multimodal sentiment analysis. However, existing methods ignore the redundancy of auxiliary modalities. More importantly, existing methods only attend to top-down attention (static process) or down-top attention (implicit process), leading to the coarse-grained multimodal sentiment context. In this paper, during the preprocessing period, we first propose the multimodal dynamic enhanced block to capture the intra-modality sentiment context. This can effectively decrease the intra-modality redundancy of auxiliary modalities. Furthermore, the bi-direction attention block is proposed to capture fine-grained multimodal sentiment context via the novel bi-direction multimodal dynamic routing mechanism. Specifically, the bi-direction attention block first highlights the explicit and low-level multimodal sentiment context. Then, the low-level multimodal context is transmitted to a carefully designed bi-direction multimodal dynamic routing procedure. This allows us to dynamically update and investigate high-level and much more fine-grained multimodal sentiment contexts. The experiments demonstrate that our fusion network can achieve state-of-the-art performance. Notably, our model outperforms the best baseline on the metric ‘Acc-7’ with an improvement of 6.9%." @default.
- W4312758889 created "2023-01-05" @default.
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- W4312758889 date "2023-04-01" @default.
- W4312758889 modified "2023-10-13" @default.
- W4312758889 title "BAFN: Bi-Direction Attention Based Fusion Network for Multimodal Sentiment Analysis" @default.
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- W4312758889 doi "https://doi.org/10.1109/tcsvt.2022.3218018" @default.
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