With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from
contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the …

With the explosive growth of multimedia contents, multimedia retrieval is facing
unprecedented challenges on both storage cost and retrieval speed. Hashing technique can …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

Recent days witness significant progress in various multi-modal tasks made by Contrastive
Language-Image Pre-training (CLIP), a multi-modal large-scale model that learns visual …

Unsupervised cross-modal hashing has attracted considerable attention to support large-
scale cross-modal retrieval. Although promising progresses have been made so far, existing …

Recently, numerous unsupervised cross-modal hashing methods have been proposed to
deal the image-text retrieval tasks for the unlabeled cross-modal data. However, when these …

Owing to its query and storage efficiency, hash learning has sparked much interest for Cross-
Modal Retrieval (CMR) task. Previous literatures have proved the superiority of supervised …

To explore the rich information contained in multi-modal data and take into account
efficiency, deep cross-modal hash retrieval (DCMHR) is a wise solution. But currently, most …

Cross-modal hashing that leverages hash functions to project high-dimensional data from
different modalities into the compact common hamming space, has shown immeasurable …