  Video-based Emotional Reaction Intensity (ERI) estimation measures the
intensity of subjects' reactions to stimuli along several emotional dimensions
from videos of the subject as they view the stimuli. We propose a multi-modal
architecture for video-based ERI combining video and audio information. Video
input is encoded spatially first, frame-by-frame, combining features encoding
holistic aspects of the subjects' facial expressions and features encoding
spatially localized aspects of their expressions. Input is then combined across
time: from frame-to-frame using gated recurrent units (GRUs), then globally by
a transformer. We handle variable video length with a regression token that
accumulates information from all frames into a fixed-dimensional vector
independent of video length. Audio information is handled similarly: spectral
information extracted within each frame is integrated across time by a cascade
of GRUs and a transformer with regression token. The video and audio regression
tokens' outputs are merged by concatenation, then input to a final fully
connected layer producing intensity estimates. Our architecture achieved
excellent performance on the Hume-Reaction dataset in the ERI Esimation
Challenge of the Fifth Competition on Affective Behavior Analysis in-the-Wild
(ABAW5). The Pearson Correlation Coefficients between estimated and subject
self-reported scores, averaged across all emotions, were 0.455 on the
validation dataset and 0.4547 on the test dataset, well above the baselines.
The transformer's self-attention mechanism enables our architecture to focus on
the most critical video frames regardless of length. Ablation experiments
establish the advantages of combining holistic/local features and of
multi-modal integration. Code available at https://github.com/HKUST-NISL/ABAW5.
