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 …

In this paper, we propose a novel deep generative approach to cross-modal retrieval to
learn hash functions in the absence of paired training samples through the cycle consistency …

Supervised deep learning-based hash and vector quantization are enabling fast and large-
scale image retrieval systems. By fully exploiting label annotations, they are achieving …

Conventional unsupervised hashing methods usually take advantage of similarity graphs,
which are either pre-computed in the high-dimensional space or obtained from random …

Due to storage and search efficiency, hashing has become significantly prevalent for nearest
neighbor search. Particularly, deep hashing methods have greatly improved the search …

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 …

Unsupervised hashing is important for indexing huge image or video collections without
having expensive annotations available. Hashing aims to learn short binary codes for …

Hashing is becoming increasingly popular for approximate nearest neighbor searching in
massive databases due to its storage and search efficiency. Recent supervised hashing …

Hashing plays a pivotal role in nearest-neighbor searching for large-scale image retrieval.
Recently, deep learning-based hashing methods have achieved promising performance …

Many unsupervised hashing methods are implicitly established on the idea of reconstructing
the input data, which basically encourages the hashing codes to retain as much information …