Deep learning for approximate nearest neighbour search: A survey and future directions
Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential
and fundamental operation in many applications from many domains such as multimedia …
and fundamental operation in many applications from many domains such as multimedia …
A survey on deep hashing methods
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 …
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
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 …
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …
Improved deep unsupervised hashing via prototypical learning
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 …
years due to its storage and computational efficiency. While deep unsupervised hashing has …
A statistical approach to mining semantic similarity for deep unsupervised hashing
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 …
semantic similarity information and then learn to map images into compact hash codes while …
Semi-supervised semi-paired cross-modal hashing
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 …
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 …
application in large-scale multimedia retrieval. Compared with traditional hashing algorithms …
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing
Unsupervised fine-grained image hashing aims to learn compact binary hash codes in
unsupervised settings addressing challenges posed by large-scale datasets and …
unsupervised settings addressing challenges posed by large-scale datasets and …
Toward effective domain adaptive retrieval
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 …
explored but emerging for efficient image retrieval, particularly for cross-domain retrieval …
Proxy-based graph convolutional hashing for cross-modal retrieval
Cross-modal hashing retrieval approaches have received extensive attention owing to their
storage superiority and retrieval efficiency. To achieve better retrieval performances …
storage superiority and retrieval efficiency. To achieve better retrieval performances …