Recently, numerous proxy hash code based methods, which sufficiently exploit the label
information of data to supervise the training of hashing models, have been proposed …

Deep unsupervised hashing has been appreciated in the regime of image retrieval.
However, most prior arts failed to detect the semantic components and their relationships …

Unsupervised deep hashing has demonstrated significant advancements with the
development of contrastive learning. However, most of previous methods have been …

Image retrieval has become an increasingly appealing technique with broad multimedia
application prospects, where deep hashing serves as the dominant branch towards low …

Due to the excellent computing efficiency, learning to hash has acquired broad popularity for
Big Data retrieval. Although supervised hashing methods have achieved promising …

Hashing, which encodes raw data into compact binary codes, has grown in popularity for
large-scale image retrieval due to its storage and computation efficiency. Although deep …

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised
hashing methods have been proposed by designing a semantic similarity matrix, which is …

Deep hashing has been widely used in image retrieval tasks, while deep hashing networks
are vulnerable to adversarial example attacks. To improve the deep hashing networks' …

As an important problem in searching system development, domain adaptive retrieval seeks
to train a retrieval model with both labeled source samples and unlabeled target samples …

Product quantization is an effective strategy for compact feature learning in image retrieval,
which generates compact quantization codes of different lengths for varying scenarios …