Multimodal Representation Learning Conditioned on Semantic Relations

Researchers have introduced Relation-Conditioned Multimodal Learning (RCML), a novel framework designed to enhance multimodal representation learning by incorporating semantic relations as explicit conditions. Unlike traditional contrastive models such as CLIP, which generate static, relation-agnostic embeddings for image-text pairs, RCML allows samples to be represented differently depending on the specific relational context described in natural language. The framework achieves this by constructing relation-aware training pairs and utilizing a specialized module to adapt embeddings to relation semantics, all optimized through a unified contrastive objective. Experimental results across multiple datasets demonstrate that RCML consistently outperforms strong baselines in zero-shot, fine-tuned, and out-of-domain settings for both retrieval and classification tasks. This approach highlights the significant effectiveness of leveraging semantic relations to guide the learning process, addressing the limitation of single-embedding models in capturing the nuanced, relation-dependent relevance inherent in real-world multimodal data applications.