Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …

Hashing has become increasingly popular in approximate nearest neighbor search in recent
years due to its storage and computational efficiency. While deep unsupervised hashing has …

The majority of deep unsupervised hashing methods usually first construct pairwise
semantic similarity information and then learn to map images into compact hash codes while …

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 …

Learning to hash pictures a list-wise sorting problem. Its testing metrics, eg, mean-average
precision, count on a sorted candidate list ordered by pair-wise code similarity. However …

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

In this paper, we study deep unsupervised hashing, a critical problem for approximate
nearest neighbor research. Most recent methods solve this problem by semantic similarity …

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 …