Title: Multimodal Representation Learning Conditioned on Semantic Relations
Primary area: foundation or frontier models, including LLMs
Keywords: Multimodal Representation Learning, Relation-Conditioned Learning

Abstract: Multimodal representation learning has advanced rapidly with contrastive models such as CLIP, which align image-text pairs in a shared embedding space. However, these models face the limitations: (1) they typically focus on image-text pairs, underutilizing the semantic relations across different pairs. (2) they directly match global embeddings without contextualization, overlooking the need for semantic alignment along specific subspaces or relational dimensions. To address these issues, we propose Relation-Conditioned Multimodal Learning (RCML), a framework that learns multimodal representations under natural-language relation descriptions to guide both feature extraction and alignment. Our approach constructs many-to-many training pairs linked by semantic relations and introduces a relation-guided cross-attention mechanism that modulates multimodal representations under each relation context. The training objective combines cross-modal and intra-modal contrastive losses, encouraging consistency across both modalities and semantically related samples. Experiments on different datasets show that RCML,consistently outperforms strong baselines on both retrieval and classification tasks, highlighting the effectiveness of leveraging semantic relations to guide multimodal representation learning.