A deep hashing model typically has two main learning objectives: to make the learned
binary hash codes discriminative and to minimize a quantization error. With further …

Multi-modal hashing fuses multi-modal features at both offline training and online query
stage for compact binary hash learning. It has aroused extensive attention in research filed …

In recent years, many achievements have been made in improving the performance of
supervised cross-modal hashing. However, it remains an open issue on how to fully explore …

Cross-modal hashing has been widely investigated recently for its efficiency in large-scale
cross-media retrieval. However, most existing cross-modal hashing methods learn hash …

Hashing has become increasingly popular in approximate nearest neighbor search in recent
years due to its storage and computational efficiency. While deep unsupervised hashing has …

The majority of deep unsupervised hashing methods usually first construct pairwise
semantic similarity information and then learn to map images into compact hash codes while …

Hashing has become increasingly important for large-scale image retrieval. Recently, deep
supervised hashing has shown promising performance, yet little work has been done under …

Online image hashing aims to update hash functions on-the-fly along with newly arriving
data streams, which has found broad applications in computer vision and beyond. To this …

When facing large-scale image datasets, online hashing serves as a promising solution for
online retrieval and prediction tasks. It encodes the online streaming data into compact …

This paper studies the problem of unsupervised domain adaptive hashing, which is less-
explored but emerging for efficient image retrieval, particularly for cross-domain retrieval …