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

FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild