Multimodal Language Analysis with Recurrent Multistage Fusion
Table 5: Comparison studies of RMFN on CMU-MOSI. Modeling cross-modal interactions using multistage fusion and attention weights are crucial in multimodal language analysis.
['Dataset', '[BOLD] CMU-MOSI', '[BOLD] CMU-MOSI', '[BOLD] CMU-MOSI', '[BOLD] CMU-MOSI', '[BOLD] CMU-MOSI']
RMFN shows improved performance, indicating that multistage fusion is both effective and efficient for human multimodal language modeling. Q4: RMFN (no MFP) represents a system of LSTHMs without the integration of zt from the MFP to model cross-modal interactions. Q5: RMFN (no HIGHLIGHT) removes the HIGHLIGHT module from MFP during multistage fusion.