Deep learning for approximate nearest neighbour search: A survey and future directions

M Li, YG Wang, P Zhang, H Wang, L Fan… - … on Knowledge and …, 2022 - ieeexplore.ieee.org
Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …

A survey on deep hashing methods

X Luo, H Wang, D Wu, C Chen, M Deng… - ACM Transactions on …, 2023 - dl.acm.org
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 …

Idea: An invariant perspective for efficient domain adaptive image retrieval

H Wang, H Wu, J Sun, S Zhang… - Advances in …, 2023 - proceedings.neurips.cc
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 …

Improved deep unsupervised hashing via prototypical learning

Z Ma, W Ju, X Luo, C Chen, XS Hua, G Lu - Proceedings of the 30th ACM …, 2022 - dl.acm.org
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 …

A statistical approach to mining semantic similarity for deep unsupervised hashing

X Luo, D Wu, Z Ma, C Chen, M Deng, J Huang… - Proceedings of the 29th …, 2021 - dl.acm.org
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 …

Semi-supervised semi-paired cross-modal hashing

X Zhang, X Liu, X Nie, X Kang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Large-scale cross-modal hashing has drawn extensive attention due to its attractive
efficiency in both storage and retrieval. Existing methods exhibit poor performance when …

Deep adaptive quadruplet hashing with probability sampling for large-scale image retrieval

Q Qin, L Huang, K Xie, Z Wei, C Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the preferable efficiency in storage and computation, hashing has shown potential
application in large-scale multimedia retrieval. Compared with traditional hashing algorithms …

An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing

F Hu, C Zhang, J Guo, XS Wei… - Proceedings of the …, 2024 - openaccess.thecvf.com
Unsupervised fine-grained image hashing aims to learn compact binary hash codes in
unsupervised settings addressing challenges posed by large-scale datasets and …

Toward effective domain adaptive retrieval

H Wang, J Sun, X Luo, W Xiang… - … on Image Processing, 2023 - ieeexplore.ieee.org
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 …

Proxy-based graph convolutional hashing for cross-modal retrieval

Y Bai, Z Shu, J Yu, Z Yu, XJ Wu - IEEE Transactions on Big …, 2023 - ieeexplore.ieee.org
Cross-modal hashing retrieval approaches have received extensive attention owing to their
storage superiority and retrieval efficiency. To achieve better retrieval performances …