Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and
data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some …

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced
research on image matching for more than a decade. In this paper, a critical review of the …

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer
science with numerous applications in (eg,) machine learning and data mining. Recent …

For efficiently retrieving nearest neighbors from large-scale multiview data, recently hashing
methods are widely investigated, which can substantially improve query speeds. In this …

Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of
data mining, machine learning and computer vision. The performance of traditional …

The recent popularity of learned embeddings has fueled the growth of massive collections of
high-dimensional (high-d) vectors that model complex data. Finding similar vectors in these …

Approximate nearest neighbor search (ANNS) in high-dimensional space is essential in
database and information retrieval. Recently, there has been a surge of interest in exploring …

Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern
applications, such as a fast search procedure with two-tower deep learning models. Graph …