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arXiv The work proposes Relation-Conditioned Multimodal Learning (RCML), a framework that treats natural-language relation descriptions as explicit conditions of multimodal representation learning by constructing relation-aware training pairs, introducing a relation-conditioned module to adapt embeddings to relation semantics, and employing a unified contrastive objective that jointly models cross-modal alignment and relation-induced inter-sample structure, so the same sample can be represented differently under different relational contexts; experiments on multiple datasets show RCML consistently outperforms strong baselines on retrieval and classification tasks in zero-shot, fine-tuned, and out-of-domain settings.
The work proposes Relation-Conditioned Multimodal Learning (RCML), a framework that treats natural-language relation descriptions as explicit conditions of multimodal representation learning by constructing relation-aware training pairs, introducing a relation-conditioned module to adapt embeddings to relation semantics, and employing a unified contrastive objective that jointly models cross-modal alignment and relation-induced inter-sample structure, so the same sample can be represented differently under different relational contexts; experiments on multiple datasets show RCML consistently outperforms strong baselines on retrieval and classification tasks in zero-shot, fine-tuned, and out-of-domain settings.
The work proposes Relation-Conditioned Multimodal Learning (RCML), a framework that treats natural-language relation descriptions as explicit conditions of multimodal representation learning by constructing relation-aware training pairs, introducing a relation-conditioned module to adapt embeddings to relation semantics, and employing a unified contrastive objective that jointly models cross-modal alignment and relation-induced inter-sample structure, so the same sample can be represented differently under different relational contexts; experiments on multiple datasets show RCML consistently outperforms strong baselines on retrieval and classification tasks in zero-shot, fine-tuned, and out-of-domain settings.
The work proposes Relation-Conditioned Multimodal Learning (RCML), a framework that treats natural-language relation descriptions as explicit conditions of multimodal representation learning by constructing relation-aware training pairs, introducing a relation-conditioned module to adapt embeddings to relation semantics, and employing a unified contrastive objective that jointly models cross-modal alignment and relation-induced inter-sample structure, so the same sample can be represented differently under different relational contexts; experiments on multiple datasets show RCML consistently outperforms strong baselines on retrieval and classification tasks in zero-shot, fine-tuned, and out-of-domain settings.