Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …

In this paper, we investigate the problem of unsupervised domain adaptive hashing, which
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …

Zero-shot sketch-based image retrieval (ZS-SBIR) has attracted great attention recently, due
to the potential application of sketch-based retrieval under zero-shot scenarios, where the …

This paper studies the problem of unsupervised domain adaptive hashing, which is less-
explored but emerging for efficient image retrieval, particularly for cross-domain retrieval …

This paper studies unsupervised domain adaptive hashing, which aims to transfer a hashing
model from a label-rich source domain to a label-scarce target domain. Current state-of-the …

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

Unsupervised domain adaptive hashing is a highly promising research direction within the
field of retrieval. It aims to transfer valuable insights from the source domain to the target …

Conventional hash-based retrieval method rely on the assumption that the query and
database are of the identical domain. However, cross-domain problem often occurs in real …

Thanks to the efficient retrieval speed and low storage consumption, learning to hash has
been widely used in visual retrieval tasks. However, the known hashing methods assume …