Authors
Wenfei Cao, Jian Xu, Yinghao Li, Huizi Yan, Zhuowei Hu, Wenxu Chen
Published in
PloS one. Volume 21. Issue 9. Pages e0359425. Epub Sep 28, 2026.
Abstract
Language use, vocal behavior, and facial activity provide complementary indicators of affective state, but multimodal models can be affected by noisy temporal observations and insufficient global guidance before cross-modal interaction. We developed a framework combining bidirectional gated recurrent unit encoders, attention pooling, dynamic hypernode injection, and graph attention fusion. Textual, acoustic, and visual sequences were mapped into a shared latent space and compressed into modality-level representations. A sample-dependent hypernode and a learnable static prior were then injected through gated residual connections before graph propagation. The model was evaluated on CMU-MOSI and CMU-MOSEI using five random seeds and validation-MAE checkpoint selection. On CMU-MOSI, the model obtained an MAE of 0.7290±0.0099, a correlation of 0.7880±0.0076, and an Acc-7 of 0.4561±0.0160. On CMU-MOSEI, the corresponding results were 0.5541±0.0053, 0.7551±0.0058, and 0.5269±0.0064. A five-run comparison of pre-GAT, post-GAT, and no injection showed that pre-GAT injection provided the strongest overall regression-oriented balance, although post-GAT was slightly higher on MOSEI Acc-7. Gate, graph-attention, representation, and balanced case diagnostics showed that the injected prior was sample dependent and modality selective. These findings support pre-propagation hypernode injection under complete, word-aligned tri-modal inputs, without establishing universal superiority or robustness to missing and corrupted modalities.
PMID:
42804527
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.
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