We address the challenge of detecting questionable content in online media,
specifically the subcategory of comic mischief. This type of content combines
elements such as violence, adult content, or sarcasm with humor, making it
difficult to detect. Employing a multimodal approach is vital to capture the
subtle details inherent in comic mischief content. To tackle this problem, we
propose a novel end-to-end multimodal system for the task of comic mischief
detection. As part of this contribution, we release a novel dataset for the
targeted task consisting of three modalities: video, text (video captions and
subtitles), and audio. We also design a HIerarchical Cross-attention model with
CAPtions (HICCAP) to capture the intricate relationships among these
modalities. The results show that the proposed approach makes a significant
improvement over robust baselines and state-of-the-art models for comic
mischief detection and its type classification. This emphasizes the potential
of our system to empower users, to make informed decisions about the online
content they choose to see. In addition, we conduct experiments on the UCF101,
HMDB51, and XD-Violence datasets, comparing our model against other
state-of-the-art approaches showcasing the outstanding performance of our
proposed model in various scenarios.

我们提出了一种新颖的端到端多模态系统用于探测在线媒体中的可疑内容，特别是针对喜剧恶作剧这一子类别。