Results 161 to 170 of about 407 (177)

Supervised Hierarchical Cross-Modal Hashing

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019
Recently, due to the unprecedented growth of multimedia data, cross-modal hashing has gained increasing attention for the efficient cross-media retrieval. Typically, existing methods on cross-modal hashing treat labels of one instance independently but overlook the correlations among labels. Indeed, in many real-world scenarios, like the online fashion
Changchang Sun, Xuemeng Song, Fuli Feng, Wayne Xin Zhao, Hao Zhang, Liqiang Nie   +5 more
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Weakly-supervised Cross-modal Hashing

IEEE Transactions on Big Data, 2019
Cross-modal hashing can efficiently retrieve data across different modalities and has been successfully applied in various domains. Although many supervised cross-modal hashing methods have been proposed, they generally focus on two modals only and assume that the labels of training data are sufficient and complete.
Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Guoqiang Xiao, Maozu Guo   +5 more
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Asymmetric Discrete Cross-Modal Hashing

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018
Recently, cross-modal hashing (CMH) methods have attracted much attention. Many methods have been explored; however, there are still some issues that need to be further considered. 1) How to efficiently construct the correlations among heterogeneous modalities.
Xin Luo, Peng-Fei Zhang, Ye Wu, Zhen-Duo Chen, Hua-Junjie Huang, Xin-Shun Xu   +5 more
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Frustratingly Easy Cross-Modal Hashing

Proceedings of the 24th ACM international conference on Multimedia, 2016
Cross-modal hashing has attracted considerable attention due to its low storage cost and fast retrieval speed. Recently, more and more sophisticated researches related to this topic are proposed. However, they seem to be inefficient computationally for several reasons.
Dekui Ma, Jian Liang, Xiangwei Kong, Ran He   +3 more
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Completely Unsupervised Cross-Modal Hashing

2020
Cross-modal hashing is an effective and practical way for large-scale multimedia retrieval. Unsupervised hashing, which is a strong candidate for cross-modal hashing, has received more attention due to its easy unlabeled data collection. However, although there has been a rich line of such work in academia, they are hindered by a common disadvantage ...
Jiasheng Duan, Pengfei Zhang, Zi Huang
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Asymmetric Deep Cross-modal Hashing

2019
Cross-modal retrieval has attracted increasing attention in recent years. Deep supervised hashing methods have been widely used for cross-modal similarity retrieval on large-scale datasets, because the deep architectures can generate more discriminative feature representations.
Jingzi Gu, JinChao Zhang, Zheng Lin, Bo Li, Weiping Wang, Dan Meng   +5 more
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Cross-Modal Hashing

2023
Lei Zhu, Jingjing Li, Weili Guan
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Continuous cross-modal hashing

Pattern Recognition, 2023
Hao Zheng, Jinbao Wang, Xiantong Zhen, Jingkuan Song, Feng Zheng, Ke Lu, Guo-Jun Qi   +6 more
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Deep Cross-modal Proxy Hashing

IEEE Transactions on Knowledge and Data Engineering, 2022
Rong-Cheng Tu, Xian-Ling Mao, Rong-Xin Tu, Binbin Bian, Chengfei Cai, Hongfa wang, Wei Wei, Heyan Huang   +7 more
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Cross-Modal Hashing via Diverse Instances Matching

IEEE Transactions on Image Processing
Cross-modal hashing is a highly effective technique for searching relevant data across different modalities, owing to its low storage costs and fast similarity retrieval capability. While significant progress has been achieved in this area, prior investigations predominantly concentrate on a one-to-one feature alignment approach, where a singular ...
Junfeng Tu, Xueliang Liu, Zhen Huang, Yanbin Hao, Richang Hong, Meng Wang   +5 more
openaire   +2 more sources