The growing importance of multi-modal humor detection within affective
computing correlates with the expanding influence of short-form video sharing
on social media platforms. In this paper, we propose a novel two-branch
hierarchical model for short-form video humor detection (SVHD), named
Comment-aided Video-Language Alignment (CVLA) via data-augmented multi-modal
contrastive pre-training. Notably, our CVLA not only operates on raw signals
across various modal channels but also yields an appropriate multi-modal
representation by aligning the video and language components within a
consistent semantic space. The experimental results on two humor detection
datasets, including DY11k and UR-FUNNY, demonstrate that CVLA dramatically
outperforms state-of-the-art and several competitive baseline approaches. Our
dataset, code and model release at this https URL

提出了一种新颖的两分支分层模型用于短视频幽默检测 (SVHD)，命名为 Comment-aided Video-Language Alignment (CVLA)，该模型通过数据增强的多模态对比性预训练，在各种模态通道上对原始信号进行操作，并通过在一致的语义空间中对齐视频和语言组件，得到合适的多模态表示。实验结果表明，CVLA 在 DY11k 和 UR-FUNNY 两个幽默检测数据集上远远超越了最先进的方法和几种竞争性基准方法。