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 …

Hashing methods have been extensively applied to efficient multimedia data indexing and
retrieval on account of the explosion of multimedia data. Cross-modal hashing usually …

Recent years have witnessed the wide application of hashing for large-scale image retrieval,
because of its high computation efficiency and low storage cost. Particularly, benefiting from …

In this paper, we propose a ranking-based locality sensitive hashing inspired two-factor
cancelable biometrics, dubbed “Index-of-Max”(IoM) hashing for biometric template …

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

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 hashing can desirably support scalable content-based image retrieval for its
appealing advantages of semantic label independence, memory, and search efficiency …

In this paper, we study the problem of cross-modal retrieval by hashing-based approximate
nearest neighbor search techniques. Most existing cross-modal hashing works mainly …

With explosive growth of data volume and ever-increasing diversity of data modalities, cross-
modal similarity search, which conducts nearest neighbor search across different modalities …