  Image-Text Retrieval (ITR) is challenging in bridging visual and lingual
modalities. Contrastive learning has been adopted by most prior arts. Except
for limited amount of negative image-text pairs, the capability of constrastive
learning is restricted by manually weighting negative pairs as well as
unawareness of external knowledge. In this paper, we propose our novel Coupled
Diversity-Sensitive Momentum Constrastive Learning (CODER) for improving
cross-modal representation. Firstly, a novel diversity-sensitive contrastive
learning (DCL) architecture is invented. We introduce dynamic dictionaries for
both modalities to enlarge the scale of image-text pairs, and
diversity-sensitiveness is achieved by adaptive negative pair weighting.
Furthermore, two branches are designed in CODER. One learns instance-level
embeddings from image/text, and it also generates pseudo online clustering
labels for its input image/text based on their embeddings. Meanwhile, the other
branch learns to query from commonsense knowledge graph to form concept-level
descriptors for both modalities. Afterwards, both branches leverage DCL to
align the cross-modal embedding spaces while an extra pseudo clustering label
prediction loss is utilized to promote concept-level representation learning
for the second branch. Extensive experiments conducted on two popular
benchmarks, i.e. MSCOCO and Flicker30K, validate CODER remarkably outperforms
the state-of-the-art approaches.
