Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance
that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal …

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

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

Latent user representations are widely adopted in the tech industry for powering
personalized recommender systems. Most prior work infers a single high dimensional …

Many recent private set intersection (PSI) protocols encode input sets as polynomials. We
consider the more general notion of an oblivious key-value store (OKVS), which is a data …

In this article, we give an overview of efficient algorithms for the approximate and exact
nearest neighbor problem. The goal is to preprocess a dataset of objects (eg, images) so …

Many binary code encoding schemes based on hashing have been actively studied
recently, since they can provide efficient similarity search, especially nearest neighbor …

Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired
multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But …

It is well known that high-dimensional nearest neighbor retrieval is very expensive. Dramatic
performance gains are obtained using approximate search schemes, such as the popular …