  Automatically understanding funny moments (i.e., the moments that make people
laugh) when watching comedy is challenging, as they relate to various features,
such as body language, dialogues and culture. In this paper, we propose
FunnyNet-W, a model that relies on cross- and self-attention for visual, audio
and text data to predict funny moments in videos. Unlike most methods that rely
on ground truth data in the form of subtitles, in this work we exploit
modalities that come naturally with videos: (a) video frames as they contain
visual information indispensable for scene understanding, (b) audio as it
contains higher-level cues associated with funny moments, such as intonation,
pitch and pauses and (c) text automatically extracted with a speech-to-text
model as it can provide rich information when processed by a Large Language
Model. To acquire labels for training, we propose an unsupervised approach that
spots and labels funny audio moments. We provide experiments on five datasets:
the sitcoms TBBT, MHD, MUStARD, Friends, and the TED talk UR-Funny. Extensive
experiments and analysis show that FunnyNet-W successfully exploits visual,
auditory and textual cues to identify funny moments, while our findings reveal
FunnyNet-W's ability to predict funny moments in the wild. FunnyNet-W sets the
new state of the art for funny moment detection with multimodal cues on all
datasets with and without using ground truth information.
