Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment
This work addresses the challenge of integrating information from different modalities for tasks like sarcasm detection and sentiment analysis, offering a simple, knowledge-free method that can be applied to other multi-modal domains.
The paper tackles the problem of multi-modal semantic understanding by proposing a CLIP-guided contrastive-learning architecture for cross-modal feature alignment, achieving significant performance gains on multi-modal sarcasm detection and sentiment analysis tasks compared to baselines.
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.