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 emerged as powerful tools for graph classification
tasks. However, contemporary graph classification methods are predominantly studied in …

In real-world datasets, visually related images often form clusters, and these clusters can be
further grouped into larger categories with more general semantics. These inherent …

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 …

Compressing videos into binary codes can improve retrieval speed and reduce storage
overhead. However, learning accurate hash codes for video retrieval can be challenging …

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 …

Hashing has achieved great success in multimedia retrieval due to its high computing
efficiency and low storage cost. Recently, contrastive-learning-based hashing methods have …

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 …

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 …