The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

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 …

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 …

In recent years a vast amount of visual content has been generated and shared from various
fields, such as social media platforms, medical images, and robotics. This abundance of …

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 …

The high efficiency in computation and storage makes hashing (including binary hashing
and quantization) a common strategy in large-scale retrieval systems. To alleviate the …

With the current exponential growth of video-based social networks, video retrieval using
natural language is receiving ever-increasing attention. Most existing approaches tackle this …

In this paper, we investigate the problem of unsupervised domain adaptive hashing, which
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …

Fashion is the manner in which we introduce ourselves to the world and has become
perhaps the biggest industry on the planet. In recent years, fashion-related research has …

Unsupervised hashing is an important approach for large-scale content-based remote
sensing (RS) image retrieval. Existing unsupervised hashing methods usually utilize data …