Cross-modal hashing has been widely investigated recently for its efficiency in large-scale
cross-media retrieval. However, most existing cross-modal hashing methods learn hash …

Cross-modal hashing hasrecently gained an increasing attention for its efficiency and fast
retrieval speed in indexing the multimedia data across different modalities. Nevertheless, the …

Concept drift is prevalent in non-stationary data environments but is rarely researched in
image retrieval. Therefore, more research is needed on image retrieval in non-stationary …

With the continuous development of mobile devices, the number of images on the Internet
increases explosively. Hashing methods solve retrieval problems with large datasets by …

Cross-view hashing has shown great potential for large-scale retrieval due to its superiority
in terms of computation and storage. In real-world applications, data emerges in a streaming …

With the increasing adoption of human-computer interaction, there is a growing trend of
extracting the image through hand-drawn sketches by humans to find out correlated objects …

In recent years, with the continuous growth of multimedia data on the Internet, multimodal
hashing has attracted increasing attention for its efficiency in large-scale cross-modal …

Deep hashing methods utilize an end-to-end framework to mutually learn feature
representations and hash codes, thereby achieving a better retrieval performance …

In the world of big data, large amounts of images are available in social media, corporate
and even personal collections. A collection may grow quickly as new images are generated …

This study explores the critical role of STEM education in equipping students for the
challenges of the 21st century and examines the effectiveness of Online Learning …