Aligning the True Semantics: Constrained Decoupling and Distribution Sampling for Cross-Modal Alignment
Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit similar semantics. Traditional algorithms pursue embedding consistency to achieve semantic consistency, ignoring the non-semantic information present in the embedding. An intuitive approach is to decouple the embeddings into semantic and modality components, aligning only the semantic component. However, this introduces two main challenges: (1) There is no established standard for distinguishing semantic and modal information. (2) The modality gap can cause semantic alignment deviation or information loss. To align ...
| Authors | Xiang Ma, Lexin Fang, Litian Xu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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