Results 21 to 30 of about 26,920 (307)
PLDH: Pseudo-Labels Based Deep Hashing
Mathematics, 2023 Deep hashing has received a great deal of attraction in large-scale data analysis, due to its high efficiency and effectiveness. The performance of deep hashing models heavily relies on label information, which is very expensive to obtain.
Huawen Liu +6 more
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Deep Discrete Supervised Hashing [PDF]
IEEE Transactions on Image Processing, 2018 Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a
Qing-Yuan Jiang, Xue Cui, Wu-Jun Li
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Unsupervised Deep Pairwise Hashing [PDF]
Electronics, 2022 Although unsupervised deep hashing is potentially very useful for tackling many large-scale tasks, its performance is still far below satisfactory. Additionally, its performance might be significantly improved by effectively exploiting the pair similarity relationship among training data, but the attained similarity matrix usually contains noisy ...
Ye Ma +3 more
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Deep Feature Pyramid Hashing for Efficient Image Retrieval
Information, 2022 Thanks to the success of deep learning, deep hashing has recently evolved as a leading method for large-scale image retrieval. Most existing hashing methods use the last layer to extract semantic information from the input image.
Adil Redaoui, Kamel Belloulata
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Quadruplet‐Based Deep Cross‐Modal Hashing
Computational Intelligence and Neuroscience, 2021 Recently, benefitting from the storage and retrieval efficiency of hashing and the powerful discriminative feature extraction capability of deep neural networks, deep cross‐modal hashing retrieval has drawn more and more attention. To preserve the semantic similarities of cross‐modal instances during the hash mapping procedure, most existing deep cross‐
Huan Liu +4 more
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Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval [PDF]
, 2020 This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features.
Mtope, Franck Romuald Fotso, Wei, Bo
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Deep Variational and Structural Hashing
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 In this paper, we propose a deep variational and structural hashing (DVStH) method to learn compact binary codes for multimedia retrieval. Unlike most existing deep hashing methods which use a series of convolution and fully-connected layers to learn binary features, we develop a probabilistic framework to infer latent feature representation inside the
Venice Erin Liong +3 more
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Unsupervised Semantic Deep Hashing [PDF]
Neurocomputing, 2018 In recent years, deep hashing methods have been proved to be efficient since it employs convolutional neural network to learn features and hashing codes simultaneously. However, these methods are mostly supervised. In real-world application, it is a time-consuming and overloaded task for annotating a large number of images.
Sheng Jin +3 more
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Dual-branch autoencoder network for attacking deep hashing image retrieval models
Dianxin kexue, 2023 Due to its powerful representation learning capabilities and efficient computing capabilities, deep learning-based hashing (deep hashing) methods are widely used in large-scale image retrieval.However, there are less studies on the security of deep ...
Sizheng FU +4 more
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Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images
IEEE Access, 2021 As a special case of perceptual hashing algorithm, subject-sensitive hashing can realize “subject-biased” integrity authentication of high resolution remote sensing (HRRS) images, which overcomes the deficiencies of existing integrity ...
Kaimeng Ding +3 more
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