Results 151 to 160 of about 407 (177)
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Unsupervised Contrastive Cross-modal Hashing
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we propose a novel momentum optimizer that performs hashing operation learnable in CL, thus making on-the ...
Peng, Hu +5 more
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Cross-modal hashing with missing labels
Neural Networks, 2023Hashing-based cross-modal retrieval methods have become increasingly popular due to their advantages in storage and speed. While current methods have demonstrated impressive results, there are still several issues that have not been addressed. Specifically, many of these approaches assume that labels are perfectly assigned, despite the fact that in ...
Haomin Ni +6 more
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Discrete online cross-modal hashing
Pattern Recognition, 2022Abstract With the prevalence of multimedia content on the Web which usually continuously comes in a stream fashion, online cross-modal hashing methods have attracted extensive interest in recent years. However, most online hashing methods adopt a relaxation strategy or real-valued auxiliary variable strategy to avoid complex optimization of hash ...
Yu-Wei Zhan +5 more
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Regularised Cross-Modal Hashing
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015In this paper we propose Regularised Cross-Modal Hashing (RCMH) a new cross-modal hashing model that projects annotation and visual feature descriptors into a common Hamming space. RCMH optimises the hashcode similarity of related data-points in the annotation modality using an iterative three-step hashing algorithm: in the first step each training ...
Sean Moran, Victor Lavrenko
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Discriminant Cross-modal Hashing
Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, 2016Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels.
Xing Xu +3 more
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Mask Cross-Modal Hashing Networks
IEEE Transactions on Multimedia, 2021Due to the rapid development of deep learning, cross-modal retrieval has achieved significant progress in recent years. Moreover, cross-modal hashing has recently attracted considerable attention to multi-modal retrieval applications due to its advantages of low storage costs and fast retrieval speed.
Qiubin Lin +3 more
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Pattern Recognition, 2018
Abstract In this paper, we present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal multimedia search. Unlike most existing cross-modal hashing methods which usually relax the optimization objective function to obtain hash codes, we develop a discrete optimization framework to jointly learn binary codes
Venice Erin Liong +2 more
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Abstract In this paper, we present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal multimedia search. Unlike most existing cross-modal hashing methods which usually relax the optimization objective function to obtain hash codes, we develop a discrete optimization framework to jointly learn binary codes
Venice Erin Liong +2 more
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Collective Reconstructive Embeddings for Cross-Modal Hashing
IEEE Transactions on Image Processing, 2019In this paper, we study the problem of cross-modal retrieval by hashing-based approximate nearest neighbor (ANN) search techniques. Most existing cross-modal hashing work mainly addresses the issue of multi-modal integration complexity using the same mapping and similarity calculation for data from different media types.
Mengqiu Hu +5 more
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2018
Cross-modal hashing enables similarity retrieval across different content modalities, such as searching relevant images in response to text queries. It provide with the advantages of computation efficiency and retrieval quality for multimedia retrieval.
Yue Cao +3 more
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Cross-modal hashing enables similarity retrieval across different content modalities, such as searching relevant images in response to text queries. It provide with the advantages of computation efficiency and retrieval quality for multimedia retrieval.
Yue Cao +3 more
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Semantic deep cross-modal hashing
Neurocomputing, 2020Abstract Because an increasing number of modality data emerge on the Internet, cross-modal retrieval has become a nontrivial research topic. Furthermore, given the massive amount of cross-modal data and the high dimension of their features, hashing has been explored because it can reduce storage cost and accelerate retrieval speed.
Qiubin Lin +3 more
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