Hashing techniques have been extensively studied in cross-modal retrieval due to their
advantages in high computational efficiency and low storage cost. However, existing …

With the efficiency of storage and retrieval speed, the hashing methods have attracted a lot
of attention for cross-modal retrieval applications. In contrast to traditional cross-modal …

Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous
modality data into the binary hash codes with discriminative semantic labels. It can support …

Due to its low storage cost and high retrieval efficiency, hashing technology is popularly
applied in both academia and industry, which provides an interesting solution for cross …

Supervised cross-modal hashing has gained increasing research interest on large-scale
retrieval task owning to its satisfactory performance and efficiency. However, there are still …

Hashing-based techniques present appealing solutions for cross-modal retrieval due to its
low storage requirements and excellent query efficiency. The majority of cross-modal …

Currently, deep hashing methods for cross-modal retrieval have achieved significant
performance. However, label-based pairwise semantic keep correspondence within bounds …

Cross-modal retrieval is a classic scenario that can describe the same semantics from
multiple angles and provide us with more abundant and diversified information; thus, it has …

The goal of cross-modal hashing is to map data from several modalities into a compact
Hamming space for efficient and accurate retrieval. Despite the satisfactory performance …

Cross-modal hashing aims at mapping data points from varied modalities to a shared
Hamming space for providing swift and adaptive search services. The recent progress in …