The explosive increase and ubiquitous accessibility of visual data on the Web have led to
the prosperity of research activity in image search or retrieval. With the ignorance of visual …

In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of
multimodal data. It takes one type of data as the query to retrieve relevant data of another …

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 …

Person re-identification (Re-ID) is an important problem in video surveillance, aiming to
match pedestrian images across camera views. Currently, most works focus on RGB-based …

Thanks to the success of deep learning, cross-modal retrieval has made significant progress
recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to …

Cross-modal hashing encodes the multimedia data into a common binary hash space in
which the correlations among the samples from different modalities can be effectively …

Due to its low storage cost and fast query speed, cross-modal hashing (CMH) has been
widely used for similarity search in multimedia retrieval applications. However, most existing …

Hashing based methods have attracted considerable attention for efficient cross-modal
retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to …

Given the benefits of its low storage requirements and high retrieval efficiency, hashing has
recently received increasing attention. In particular, cross-modal hashing has been widely …