  Multi-modal semantic understanding requires integrating information from
different modalities to extract users' real intention behind words. Most
previous work applies a dual-encoder structure to separately encode image and
text, but fails to learn cross-modal feature alignment, making it hard to
achieve cross-modal deep information interaction. This paper proposes a novel
CLIP-guided contrastive-learning-based architecture to perform multi-modal
feature alignment, which projects the features derived from different
modalities into a unified deep space. On multi-modal sarcasm detection (MMSD)
and multi-modal sentiment analysis (MMSA) tasks, the experimental results show
that our proposed model significantly outperforms several baselines, and our
feature alignment strategy brings obvious performance gain over models with
different aggregating methods and models even enriched with knowledge. More
importantly, our model is simple to implement without using task-specific
external knowledge, and thus can easily migrate to other multi-modal tasks. Our
source codes are available at https://github.com/ChangKe123/CLFA.
