Image-text matching has been a long-standing problem, which seeks to connect
vision and language through semantic understanding. Due to the capability to
manage large-scale raw data, unsupervised hashing-based approaches have gained
prominence recently. They typically construct a semantic similarity structure
using the natural distance, which subsequently provides guidance to the model
optimization process. However, the similarity structure could be biased at the
boundaries of semantic distributions, causing error accumulation during
sequential optimization. To tackle this, we introduce a novel hashing approach
termed Distribution-based Structure Mining with Consistency Learning (DEMO) for
efficient image-text matching. From a statistical view, DEMO characterizes each
image using multiple augmented views, which are considered as samples drawn
from its intrinsic semantic distribution. Then, we employ a non-parametric
distribution divergence to ensure a robust and precise similarity structure. In
addition, we introduce collaborative consistency learning which not only
preserves the similarity structure in the Hamming space but also encourages
consistency between retrieval distribution from different directions in a
self-supervised manner. Through extensive experiments on three benchmark
image-text matching datasets, we demonstrate that DEMO achieves superior
performance compared with many state-of-the-art methods.

DEMO 是一种用于高效图像 - 文本匹配的新型哈希方法，通过使用多个扩增视角来刻画每个图像，并通过非参数分布差异度量确保稳健且精准的相似性结构，同时引入协作一致性学习来在 Hamming 空间中保持相似性结构、鼓励不同方向的检索分布的一致性，并通过在三个基准图像 - 文本匹配数据集上的广泛实验验证 DEMO 相较于多种最先进方法具有卓越性能。