Supervised cross-modal hashing has gained a lot of attention recently. However, most
existing methods learn binary codes or hash functions in a batch-based scheme, which is …

Online image hashing aims to update hash functions on-the-fly along with newly arriving
data streams, which has found broad applications in computer vision and beyond. To this …

Hashing methods are widely used for content-based image retrieval due to their attractive
time and space efficiencies. Several dynamic hashing methods have been proposed for …

Batch-based cross-modal hashing retrieval methods have made great progress. However,
they could not be applied in scenarios where new data continuously arrives in a stream. To …

The batch-based hash learning paradigm for uni-modal or cross-modal retrieval has made
great progress in recent decades. However, methods that are based on this paradigm …

Online cross-modal hashing has gained attention for its adaptability in processing streaming
data. However, existing methods only define the hard similarity between data using labels …

Online image hashing has received increasing research attention recently, which processes
large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end …

With the vigorous development of multimedia equipments and applications, efficient retrieval
of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has …

With the continuous generation of massive amounts of multimedia data nowadays, hashing
has demonstrated significant potentials for large-scale search. To handle the emerging …

Online hashing for streaming data has attracted increasing attention recently. However, most
existing algorithms focus on batch inputs and instance-balanced optimization, which is …