Domain adaptive hashing has received increasing attention since it is capable of enhancing
the performance of retrieval if the target domain for testing meets domain shift. However …

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

Existing unsupervised deep product quantization methods primarily aim for the increased
similarity between different views of the identical image, whereas the delicate multi-level …

By noticing the superior ability of contrastive learning in representation learning, several
recent works have proposed to use it to learn semantic-rich hash codes. However, due to the …

Multi-label learning (MLL) usually requires assigning multiple relevant labels to each
instance. While a fully supervised MLL dataset needs a large amount of labeling effort, using …

This article studies deep unsupervised hashing, which has attracted increasing attention in
large-scale image retrieval. The majority of recent approaches usually reconstruct semantic …

Deep hashing enhances image retrieval accuracy by integrating hash encoding with deep
neural networks. However, existing unsupervised deep hashing methods primarily rely on …

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 …

The recent trend in unsupervised hashing requires not only a discrete representation space
but also the ability to mine the similarities between data points. Determining and maintaining …

Recently, numerous degraded images have flooded search engines and social networks,
finding extensive and practical applications in the real world. However, these images have …