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

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

Where am I? This is one of the most critical questions that any intelligent system should
answer to decide whether it navigates to a previously visited area. This problem has long …

Quantization based techniques are the current state-of-the-art for scaling maximum inner
product search to massive databases. Traditional approaches to quantization aim to …

There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

We present a new approach for the approximate K-nearest neighbor search based on
navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The …

To solve deep metric learning problems and produce feature embeddings, current
methodologies will commonly use a triplet model to minimise the relative distance between …

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 …

Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera
to images stored in a reference map of the environment. While initial VPR systems used …

The approximate nearest neighbor (ANN) search in high-dimensional spaces is a
fundamental but computationally very expensive problem. Many methods have been …