  We present Affect2MM, a learning method for time-series emotion prediction
for multimedia content. Our goal is to automatically capture the varying
emotions depicted by characters in real-life human-centric situations and
behaviors. We use the ideas from emotion causation theories to computationally
model and determine the emotional state evoked in clips of movies. Affect2MM
explicitly models the temporal causality using attention-based methods and
Granger causality. We use a variety of components like facial features of
actors involved, scene understanding, visual aesthetics, action/situation
description, and movie script to obtain an affective-rich representation to
understand and perceive the scene. We use an LSTM-based learning model for
emotion perception. To evaluate our method, we analyze and compare our
performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE
dataset, and observe an average of 10-15% increase in the performance over SOTA
methods for all three datasets.
