MXM-CLR: A Unified Framework for Contrastive Learning of Multifold Cross-Modal Representations

no code implementations • 20 Mar 2023 • Ye Wang, Bowei Jiang, Changqing Zou, Rui Ma

Existing cross-modal contrastive representation learning (XM-CLR) methods such as CLIP are not fully suitable for multifold data as they only consider one positive pair and treat other pairs as negative when computing the contrastive loss.

Contrastive Learning Cross-Modal Retrieval +2