  In vision and linguistics; the main input modalities are facial expressions,
speech patterns, and the words uttered. The issue with analysis of any one mode
of expression (Visual, Verbal or Vocal) is that lot of contextual information
can get lost. This asks researchers to inspect multiple modalities to get a
thorough understanding of the cross-modal dependencies and temporal context of
the situation to analyze the expression. This work attempts at preserving the
long-range dependencies within and across different modalities, which would be
bottle-necked by the use of recurrent networks and adds the concept of
delta-attention to focus on local differences per modality to capture the
idiosyncrasy of different people. We explore a cross-attention fusion technique
to get the global view of the emotion expressed through these
delta-self-attended modalities, in order to fuse all the local nuances and
global context together. The addition of attention is new to the multi-modal
fusion field and currently being scrutinized for on what stage the attention
mechanism should be used, this work achieves competitive accuracy for overall
and per-class classification which is close to the current state-of-the-art
with almost half number of parameters.

Computer Vision and Pattern Recognition;Computation and Language;Machine Learning