Factorized Multimodal Transformer for Multimodal Sequential Learning
Table 5: Multimodal sentiment analysis experiments with different number of FMS units inside MTL. The number in the square bracket indicates the number of FMS. The total number of attention is 7 times the number in bracket.
['[ITALIC] Model \\ [ITALIC] Metric', '[ITALIC] BA', '[ITALIC] F1', '[ITALIC] MAE', '[ITALIC] Corr']
We also perform experiments to understand the effect of number of FMT units within each MTL. The model with 6 number of FMS (42 attentions in total) achieves the highest performance (6 is also the highest number we experimented with). Tsai et al. FMT uses fewer number of attentions than MulT, yet achieves better performance. We also experiment with number of heads for original transformer model