  In this paper, we test the hypothesis that interesting events in unstructured
videos are inherently audiovisual. We combine deep image representations for
object recognition and scene understanding with representations from an
audiovisual affect recognition model. To this set, we include content agnostic
audio-visual synchrony representations and mel-frequency cepstral coefficients
to capture other intrinsic properties of audio. These features are used in a
modular supervised model. We present results from two experiments: efficacy
study of single features on the task, and an ablation study where we leave one
feature out at a time. For the video summarization task, our results indicate
that the visual features carry most information, and including audiovisual
features improves over visual-only information. To better study the task of
highlight detection, we run a pilot experiment with highlights annotations for
a small subset of video clips and fine-tune our best model on it. Results
indicate that we can transfer knowledge from the video summarization task to a
model trained specifically for the task of highlight detection.
