Multi-modal humor segment prediction in video

Humor can be induced by various signals in the visual, linguistic, and vocal modalities emitted by humans. Finding humor in videos is an interesting but challenging task for an intelligent system. Previous methods predict humor in the sentence level given some text (e.g., speech transcript), sometimes together with other modalities, such as videos and speech. Such methods ignore humor caused by the visual modality in their design, since their prediction is made for a sentence. In this work, we first give new annotations to humor based on a sitcom by setting up temporal segments of ground truth humor derived from the laughter track. Then, we propose a method to find these temporal segments of humor. We adopt an approach based on sliding window, where the visual modality is described by pose and facial features along with the linguistic modality given as subtitles in each sliding window. We use long short-term memory networks to encode the temporal dependency in poses and facial features and pre-trained BERT to handle subtitles. Experimental results show that our method improves the performance of humor prediction.