Results 151 to 160 of about 10,164 (194)
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A General Framework for Deep Supervised Discrete Hashing
International Journal of Computer Vision, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qi Li, Zhenan Sun, Ran He, Tieniu Tan
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Deep Supervised Hashing With Anchor Graph
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019Recently, a series of deep supervised hashing methods were proposed for binary code learning. However, due to the high computation cost and the limited hardware's memory, these methods will first select a subset from the training set, and then form a mini-batch data to update the network in each iteration.
Yudong Chen +4 more
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Adversarial Binary Mutual Learning for Semi-Supervised Deep Hashing
IEEE Transactions on Neural Networks and Learning Systems, 2022Hashing is a popular search algorithm for its compact binary representation and efficient Hamming distance calculation. Benefited from the advance of deep learning, deep hashing methods have achieved promising performance. However, those methods usually learn with expensive labeled data but fail to utilize unlabeled data.
Guan'An Wang +4 more
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Deep Supervised Hashing with Dynamic Weighting Scheme
2020 5th IEEE International Conference on Big Data Analytics (ICBDA), 2020Similarity preserving hashing methods have drawn a lot of attention lately due to their efficiency in performing approximate nearest neighbor searches on high-dimensional large-scale multimedia data. With the development of deep learning techniques, deep supervised hashing has attracted increasing attention lately. Existing deep hashing methods usually
Yingxiang Sun, Shuying Yu
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Robust deep supervised hashing for image retrieval
Twelfth International Conference on Digital Image Processing (ICDIP 2020), 2020Hashing is an important technique branch of image retrieval due to its satisfactory retrieval performance with high retrieval speed and low storage cost. Deep supervised hashing methods, which take advantage of the convolutional neural network and the supervised information, have shown better performance than other kinds of hashing methods.
Zhaoguo Mo, Yuesheng Zhu, Jiawei Zhan
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Deep Hashing with Active Pairwise Supervision
2020In this paper, we propose a Deep Hashing method with Active Pairwise Supervision (DH-APS). Conventional methods with passive pairwise supervision obtain labeled data for training and require large amount of annotations to reach their full potential, which are not feasible in realistic retrieval tasks.
Ziwei Wang +3 more
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Deep Supervised Hashing with Pairwise Bit Loss
Proceedings of the 2017 International Conference on Deep Learning Technologies, 2017Low dimensional binary hashing is the key point in large-scale image retrieval and person re-identification (re-ID). To promote the performance, we explore the possibility of deep supervised hashing using the label information. A pairwise bit loss is proposed to measure the difference between two features extracted from two intra-class images by CNNs ...
Jiabao Wang +4 more
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Piecewise supervised deep hashing for image retrieval
Multimedia Tools and Applications, 2019In this paper, we propose a novel hash code generation method based on convolutional neural network (CNN), called the piecewise supervised deep hashing (PSDH) method to directly use a latent layer data and the output layer result of the classification network to generate a two-segment hash code for every input image.
Yannuan Li, Lin Wan, Ting Fu, Weijun Hu
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Deep Semantic Text Hashing with Weak Supervision
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018With an ever increasing amount of data available on the web, fast similarity search has become the critical component for large-scale information retrieval systems. One solution is semantic hashing which designs binary codes to accelerate similarity search.
Suthee Chaidaroon, Travis Ebesu, Yi Fang
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Cosine metric supervised deep hashing with balanced similarity
Neurocomputing, 2021Abstract Deep supervised hashing takes prominent advantages of low storage cost, high computational efficiency and good retrieval performance, which draws attention in the field of large-scale image retrieval. However, similarity-preserving, quantization errors and imbalanced data are still great challenges in deep supervised hashing.
Wenjin Hu +4 more
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