  Image-text matching remains a challenging task due to heterogeneous semantic
diversity across modalities and insufficient distance separability within
triplets. Different from previous approaches focusing on enhancing multi-modal
representations or exploiting cross-modal correspondence for more accurate
retrieval, in this paper we aim to leverage the knowledge transfer between peer
branches in a boosting manner to seek a more powerful matching model.
Specifically, we propose a brand-new Deep Boosting Learning (DBL) algorithm,
where an anchor branch is first trained to provide insights into the data
properties, with a target branch gaining more advanced knowledge to develop
optimal features and distance metrics. Concretely, an anchor branch initially
learns the absolute or relative distance between positive and negative pairs,
providing a foundational understanding of the particular network and data
distribution. Building upon this knowledge, a target branch is concurrently
tasked with more adaptive margin constraints to further enlarge the relative
distance between matched and unmatched samples. Extensive experiments validate
that our DBL can achieve impressive and consistent improvements based on
various recent state-of-the-art models in the image-text matching field, and
outperform related popular cooperative strategies, e.g., Conventional
Distillation, Mutual Learning, and Contrastive Learning. Beyond the above, we
confirm that DBL can be seamlessly integrated into their training scenarios and
achieve superior performance under the same computational costs, demonstrating
the flexibility and broad applicability of our proposed method. Our code is
publicly available at: https://github.com/Paranioar/DBL.
