Results 171 to 180 of about 1,457 (180)

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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Deep high-order supervised hashing

Optik, 2019
Abstract Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised
Jing Dong Cheng, Qiu Le Sun, Jian Xin Zhang, Christian Desrosiers, Bin Liu, Jian Lu, Qiang Zhang   +6 more
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Supervised Hashing with Recurrent Scaling

2019
Learning to hash is a method that can deal with content-based information retrieval efficiently. Traditional learning to hash methods, however, lack the ability to map the generated hash codes to the high-level semantic space. Attributes, as a kind of higher level of visual data representation compared to features, have the potential ability in deep ...
Xiyao Fu, Yi Bin, Zheng Wang, Qin Wei, Si Chen   +4 more
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Supervised Representation Hash Codes Learning

2019
Learning-based hashing has been widely employed for large-scale similarity retrieval due to its efficient computation and compressed storage. In this paper, we propose ResHash, a deep representation hash code learning approach to learning compact and discriminative binary codes.
Huei-Fang Yang, Cheng-Hao Tu, Chu-Song Chen   +2 more
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Hashing with Inductive Supervised Learning

2015
Recent years have witnessed the effectiveness and efficiency of learning-based hashing methods which generate short binary codes preserving the Euclidean similarity in the original space of high dimension. However, because of their complexities and out-of-sample problems, most of methods are not appropriate for embedding of large-scale datasets.
Mingxing Zhang, Fumin Shen, Hanwang Zhang, Ning Xie, Wankou Yang   +4 more
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State Abstraction via Deep Supervised Hash Learning

IEEE Transactions on Neural Networks and Learning Systems
State abstraction is a widely used technique in reinforcement learning (RL) that compresses the state space to accelerate learning algorithms. However, designing an effective abstraction function in large-scale or high-dimensional state space problems remains a significant challenge.
Guang Yang, Zheng Xu, Jing Huo, Shangdong Yang, Tianyu Ding, Xingguo Chen, Yang Gao   +6 more
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Swin transformer-based supervised hashing

Applied Intelligence, 2023
Liangkang Peng, Jiangbo Qian, Chong Wang, Baisong Liu, Yihong Dong   +4 more
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Cosine Metric Supervised Deep Hashing

2022
Wenjin Hu, Yukun Chen, Meng Jian, Lifang Wu   +3 more
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Scalable Supervised Asymmetric Hashing

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Semi-supervised incremental hashing method

Journal of Electronic Imaging
Hai Su, Yuchen Zhong
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