  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 https://github.com/yliu-cs/CVLA.
