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Journal articles on the topic 'Cross-Modal Retrieval and Hashing'

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Author: Grafiati

Published: 6 September 2023

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1

Liu, Huan, Jiang Xiong, Nian Zhang, Fuming Liu, and Xitao Zou. "Quadruplet-Based Deep Cross-Modal Hashing." Computational Intelligence and Neuroscience 2021 (July 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/9968716.

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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-modal hashing methods usually learn deep hashing networks with a pairwise loss or a triplet loss. However, these methods may not fully explore the similarity relation across modalities. To solve this problem, in this paper, we introduce a quadruplet loss into deep cross-modal hashing and propose a quadruplet-based deep cross-modal hashing (termed QDCMH) method. Extensive experiments on two benchmark cross-modal retrieval datasets show that our proposed method achieves state-of-the-art performance and demonstrate the efficiency of the quadruplet loss in cross-modal hashing.

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2

Liu, Xuanwu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, and Maozu Guo. "Ranking-Based Deep Cross-Modal Hashing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4400–4407. http://dx.doi.org/10.1609/aaai.v33i01.33014400.

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Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications.

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3

Yang, Xiaohan, Zhen Wang, Nannan Wu, Guokun Li, Chuang Feng, and Pingping Liu. "Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing." Mathematics 10, no. 15 (July 28, 2022): 2644. http://dx.doi.org/10.3390/math10152644.

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The image-text cross-modal retrieval task, which aims to retrieve the relevant image from text and vice versa, is now attracting widespread attention. To quickly respond to the large-scale task, we propose an Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing (DRNPH) to achieve cross-modal retrieval in the common Hamming space, which has the advantages of storage and efficiency. To fulfill the nearest neighbor search in the Hamming space, we demand to reconstruct both the original intra- and inter-modal neighbor matrix according to the binary feature vectors. Thus, we can compute the neighbor relationship among different modal samples directly based on the Hamming distances. Furthermore, the cross-modal pair-wise similarity preserving constraint requires the similar sample pair have an identical Hamming distance to the anchor. Therefore, the similar sample pairs own the same binary code, and they have minimal Hamming distances. Unfortunately, the pair-wise similarity preserving constraint may lead to an imbalanced code problem. Therefore, we propose the cross-modal triplet relative similarity preserving constraint, which demands the Hamming distances of similar pairs should be less than those of dissimilar pairs to distinguish the samples’ ranking orders in the retrieval results. Moreover, a large similarity marginal can boost the algorithm’s noise robustness. We conduct the cross-modal retrieval comparative experiments and ablation study on two public datasets, MIRFlickr and NUS-WIDE, respectively. The experimental results show that DRNPH outperforms the state-of-the-art approaches in various image-text retrieval scenarios, and all three proposed constraints are necessary and effective for boosting cross-modal retrieval performance.

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Li, Chao, Cheng Deng, Lei Wang, De Xie, and Xianglong Liu. "Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 176–83. http://dx.doi.org/10.1609/aaai.v33i01.3301176.

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In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.

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5

刘, 志虎. "Label Consistency Hashing for Cross-Modal Retrieval." Computer Science and Application 11, no. 04 (2021): 1104–12. http://dx.doi.org/10.12677/csa.2021.114114.

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6

Yao, Tao, Xiangwei Kong, Haiyan Fu, and Qi Tian. "Semantic consistency hashing for cross-modal retrieval." Neurocomputing 193 (June 2016): 250–59. http://dx.doi.org/10.1016/j.neucom.2016.02.016.

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7

Chen, Shubai, Song Wu, and Li Wang. "Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval." PeerJ Computer Science 7 (May 25, 2021): e552. http://dx.doi.org/10.7717/peerj-cs.552.

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Due to the high efficiency of hashing technology and the high abstraction of deep networks, deep hashing has achieved appealing effectiveness and efficiency for large-scale cross-modal retrieval. However, how to efficiently measure the similarity of fine-grained multi-labels for multi-modal data and thoroughly explore the intermediate layers specific information of networks are still two challenges for high-performance cross-modal hashing retrieval. Thus, in this paper, we propose a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN) for large-scale cross-modal retrieval. In the proposed HSIDHN, the multi-scale and fusion operations are first applied to each layer of the network. A Bidirectional Bi-linear Interaction (BBI) policy is then designed to achieve the hierarchical semantic interaction among different layers, such that the capability of hash representations can be enhanced. Moreover, a dual-similarity measurement (“hard” similarity and “soft” similarity) is designed to calculate the semantic similarity of different modality data, aiming to better preserve the semantic correlation of multi-labels. Extensive experiment results on two large-scale public datasets have shown that the performance of our HSIDHN is competitive to state-of-the-art deep cross-modal hashing methods.

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8

Li, Mingyong, Qiqi Li, Lirong Tang, Shuang Peng, Yan Ma, and Degang Yang. "Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model." Computational Intelligence and Neuroscience 2021 (July 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/5107034.

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Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross-modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR-25K and NUS-WIDE) show that compared to several representative unsupervised cross-modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large-scale cross-modal data retrieval.

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9

Zhong, Fangming, Zhikui Chen, and Geyong Min. "Deep Discrete Cross-Modal Hashing for Cross-Media Retrieval." Pattern Recognition 83 (November 2018): 64–77. http://dx.doi.org/10.1016/j.patcog.2018.05.018.

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10

Qi, Xiaojun, Xianhua Zeng, Shumin Wang, Yicai Xie, and Liming Xu. "Cross-modal variable-length hashing based on hierarchy." Intelligent Data Analysis 25, no. 3 (April 20, 2021): 669–85. http://dx.doi.org/10.3233/ida-205162.

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Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of existing hashing methods use fixed-length hash codes, which increase the computational costs for large-size datasets. Furthermore, learning hash functions is an NP hard problem. To address these problems, we initially propose a novel method named Cross-modal Variable-length Hashing Based on Hierarchy (CVHH), which can learn the hash functions more accurately to improve retrieval performance, and also reduce the computational costs and training time. The main contributions of CVHH are: (1) We propose a variable-length hashing algorithm to improve the algorithm performance; (2) We apply the hierarchical architecture to effectively reduce the computational costs and training time. To validate the effectiveness of CVHH, our extensive experimental results show the superior performance compared with recent state-of-the-art cross-modal methods on three benchmark datasets, WIKI, NUS-WIDE and MIRFlickr.

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11

He, Chao, Dalin Wang, Zefu Tan, Liming Xu, and Nina Dai. "Cross-Modal Discrimination Hashing Retrieval Using Variable Length." Security and Communication Networks 2022 (September 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/9638683.

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Fast cross-modal retrieval technology based on hash coding has become a hot topic for the rich multimodal data (text, image, audio, etc.), especially security and privacy challenges in the Internet of Things and mobile edge computing. However, most methods based on hash coding are only mapped to the common hash coding space, and it relaxes the two value constraints of hash coding. Therefore, the learning of the multimodal hash coding may not be sufficient and effective to express the original multimodal data and cause the hash encoding category to be less discriminatory. For the sake of solving these problems, this paper proposes a method of mapping each modal data to the optimal length of hash coding space, respectively, and then the hash encoding of each modal data is solved by the discrete cross-modal hash algorithm of two value constraints. Finally, the similarity of multimodal data is compared in the potential space. The experimental results of the cross-model retrieval based on variable hash coding are better than that of the relative comparison methods in the WIKI data set, NUS-WIDE data set, as well as MIRFlickr data set, and the method we proposed is proved to be feasible and effective.

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12

Fang, Xiaozhao, Zhihu Liu, Na Han, Lin Jiang, and Shaohua Teng. "Discrete matrix factorization hashing for cross-modal retrieval." International Journal of Machine Learning and Cybernetics 12, no. 10 (August 2, 2021): 3023–36. http://dx.doi.org/10.1007/s13042-021-01395-5.

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13

Tang, Jun, Ke Wang, and Ling Shao. "Supervised Matrix Factorization Hashing for Cross-Modal Retrieval." IEEE Transactions on Image Processing 25, no. 7 (July 2016): 3157–66. http://dx.doi.org/10.1109/tip.2016.2564638.

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14

Mandal, Devraj, Kunal N. Chaudhury, and Soma Biswas. "Generalized Semantic Preserving Hashing for Cross-Modal Retrieval." IEEE Transactions on Image Processing 28, no. 1 (January 2019): 102–12. http://dx.doi.org/10.1109/tip.2018.2863040.

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15

Yao, Tao, Zhiwang Zhang, Lianshan Yan, Jun Yue, and Qi Tian. "Discrete Robust Supervised Hashing for Cross-Modal Retrieval." IEEE Access 7 (2019): 39806–14. http://dx.doi.org/10.1109/access.2019.2897249.

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16

Li, Kai, Guo-Jun Qi, Jun Ye, and Kien A. Hua. "Linear Subspace Ranking Hashing for Cross-Modal Retrieval." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 9 (September 1, 2017): 1825–38. http://dx.doi.org/10.1109/tpami.2016.2610969.

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17

Cheng, Miaomiao, Liping Jing, and Michael K. Ng. "Robust Unsupervised Cross-modal Hashing for Multimedia Retrieval." ACM Transactions on Information Systems 38, no. 3 (June 26, 2020): 1–25. http://dx.doi.org/10.1145/3389547.

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18

Lu, Xu, Huaxiang Zhang, Jiande Sun, Zhenhua Wang, Peilian Guo, and Wenbo Wan. "Discriminative correlation hashing for supervised cross-modal retrieval." Signal Processing: Image Communication 65 (July 2018): 221–30. http://dx.doi.org/10.1016/j.image.2018.04.009.

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19

Guo, Jiaen, Haibin Wang, Bo Dan, and Yu Lu. "Deep Supervised Cross-modal Hashing for Ship Image Retrieval." Journal of Physics: Conference Series 2320, no. 1 (August 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2320/1/012023.

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Abstract The retrieval of multimodal ship images obtained by remote sensing satellites is an important content of remote sensing data analysis, which is of great significance to improve the ability of marine monitoring. In this paper, We propose a novel cross-modal ship image retrieval method, called Deep Supervised Cross-modal Hashing(DSCMH). It consists of a feature learning part and a hash learning part used for feature extraction and hash code generation separately, both two parts have modality-invariant constraints to keep the cross-modal invariability, and the label information is also brought to supervise the above process. Furthermore, we design a class attention module based on the cross-modal class center to strengthen class discrimination. The experiment results show that the proposed method can effectively improve the cross-modal retrieval accuracy of ship images and is better than several state-of-the-art methods.

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20

Han, Jianan, Shaoxing Zhang, Aidong Men, and Qingchao Chen. "Cross-Modal Contrastive Hashing Retrieval for Infrared Video and EEG." Sensors 22, no. 22 (November 14, 2022): 8804. http://dx.doi.org/10.3390/s22228804.

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It is essential to estimate the sleep quality and diagnose the clinical stages in time and at home, because they are closely related to and important causes of chronic diseases and daily life dysfunctions. However, the existing “gold-standard” sensing machine for diagnosis (Polysomnography (PSG) with Electroencephalogram (EEG) measurements) is almost infeasible to deploy at home in a “ubiquitous” manner. In addition, it is costly to train clinicians for the diagnosis of sleep conditions. In this paper, we proposed a novel technical and systematic attempt to tackle the previous barriers: first, we proposed to monitor and sense the sleep conditions using the infrared (IR) camera videos synchronized with the EEG signal; second, we proposed a novel cross-modal retrieval system termed as Cross-modal Contrastive Hashing Retrieval (CCHR) to build the relationship between EEG and IR videos, retrieving the most relevant EEG signal given an infrared video. Specifically, the CCHR is novel in the following two perspectives. Firstly, to eliminate the large cross-modal semantic gap between EEG and IR data, we designed a novel joint cross-modal representation learning strategy using a memory-enhanced hard-negative mining design under the framework of contrastive learning. Secondly, as the sleep monitoring data are large-scale (8 h long for each subject), a novel contrastive hashing module is proposed to transform the joint cross-modal features to the discriminative binary hash codes, enabling the efficient storage and inference. Extensive experiments on our collected cross-modal sleep condition dataset validated that the proposed CCHR achieves superior performances compared with existing cross-modal hashing methods.

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21

Long, Jun, Longzhi Sun, Liujie Hua, and Zhan Yang. "Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval." Applied Sciences 11, no. 18 (September 21, 2021): 8769. http://dx.doi.org/10.3390/app11188769.

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Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ2,1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods.

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22

Xie, Yicai, Xianhua Zeng, Tinghua Wang, and Yun Yi. "Online deep hashing for both uni-modal and cross-modal retrieval." Information Sciences 608 (August 2022): 1480–502. http://dx.doi.org/10.1016/j.ins.2022.07.039.

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23

Williams-Lekuona, Mikel, Georgina Cosma, and Iain Phillips. "A Framework for Enabling Unpaired Multi-Modal Learning for Deep Cross-Modal Hashing Retrieval." Journal of Imaging 8, no. 12 (December 15, 2022): 328. http://dx.doi.org/10.3390/jimaging8120328.

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Cross-Modal Hashing (CMH) retrieval methods have garnered increasing attention within the information retrieval research community due to their capability to deal with large amounts of data thanks to the computational efficiency of hash-based methods. To date, the focus of cross-modal hashing methods has been on training with paired data. Paired data refers to samples with one-to-one correspondence across modalities, e.g., image and text pairs where the text sample describes the image. However, real-world applications produce unpaired data that cannot be utilised by most current CMH methods during the training process. Models that can learn from unpaired data are crucial for real-world applications such as cross-modal neural information retrieval where paired data is limited or not available to train the model. This paper provides (1) an overview of the CMH methods when applied to unpaired datasets, (2) proposes a framework that enables pairwise-constrained CMH methods to train with unpaired samples, and (3) evaluates the performance of state-of-the-art CMH methods across different pairing scenarios.

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24

Zhang, Peng-Fei, Yadan Luo, Zi Huang, Xin-Shun Xu, and Jingkuan Song. "High-order nonlocal Hashing for unsupervised cross-modal retrieval." World Wide Web 24, no. 2 (February 27, 2021): 563–83. http://dx.doi.org/10.1007/s11280-020-00859-y.

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25

Zeng, Chao, Cong Bai, Qing Ma, and Shengyong Chen. "Adversarial Projection Learning Based Hashing for Cross-Modal Retrieval." Journal of Computer-Aided Design & Computer Graphics 33, no. 6 (June 1, 2021): 904–12. http://dx.doi.org/10.3724/sp.j.1089.2021.18599.

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26

Karbil, Loubna, Ahmad Sani, and Imane Daoudi. "Collective Bayesian Matrix factorization Hashing for cross-modal retrieval." International Journal of Mathematics Trends and Technology 67, no. 3 (March 25, 2021): 57–69. http://dx.doi.org/10.14445/22315373/ijmtt-v67i3p508.

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27

Li, Weian, Haixia Xiong, Weihua Ou, Jianping Gou, Jiaxing Deng, Linqing Liang, and Quan Zhou. "Semantic Constraints Matrix Factorization Hashing for cross-modal retrieval." Computers and Electrical Engineering 100 (May 2022): 107842. http://dx.doi.org/10.1016/j.compeleceng.2022.107842.

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28

Wu, Lin, Yang Wang, and Ling Shao. "Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval." IEEE Transactions on Image Processing 28, no. 4 (April 2019): 1602–12. http://dx.doi.org/10.1109/tip.2018.2878970.

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29

Deng, Cheng, Zhaojia Chen, Xianglong Liu, Xinbo Gao, and Dacheng Tao. "Triplet-Based Deep Hashing Network for Cross-Modal Retrieval." IEEE Transactions on Image Processing 27, no. 8 (August 2018): 3893–903. http://dx.doi.org/10.1109/tip.2018.2821921.

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30

Ji, Zhenyan, Weina Yao, Wei Wei, Houbing Song, and Huaiyu Pi. "Deep Multi-Level Semantic Hashing for Cross-Modal Retrieval." IEEE Access 7 (2019): 23667–74. http://dx.doi.org/10.1109/access.2019.2899536.

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31

Qiang, Haopeng, Yuan Wan, Ziyi Liu, Lun Xiang, and Xiaojing Meng. "Discriminative deep asymmetric supervised hashing for cross-modal retrieval." Knowledge-Based Systems 204 (September 2020): 106188. http://dx.doi.org/10.1016/j.knosys.2020.106188.

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32

Qiang, Haopeng, Yuan Wan, Lun Xiang, and Xiaojing Meng. "Deep semantic similarity adversarial hashing for cross-modal retrieval." Neurocomputing 400 (August 2020): 24–33. http://dx.doi.org/10.1016/j.neucom.2020.03.032.

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33

Wang, Ke, Jun Tang, Nian Wang, and Ling Shao. "Semantic Boosting Cross-Modal Hashing for efficient multimedia retrieval." Information Sciences 330 (February 2016): 199–210. http://dx.doi.org/10.1016/j.ins.2015.10.028.

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34

Wang, Xingzhi, Xin Liu, Shu-Juan Peng, Bineng Zhong, Yewang Chen, and Ji-Xiang Du. "Semi-supervised discrete hashing for efficient cross-modal retrieval." Multimedia Tools and Applications 79, no. 35-36 (July 1, 2020): 25335–56. http://dx.doi.org/10.1007/s11042-020-09195-9.

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Ma, Xinhong, Tianzhu Zhang, and Changsheng Xu. "Multi-Level Correlation Adversarial Hashing for Cross-Modal Retrieval." IEEE Transactions on Multimedia 22, no. 12 (December 2020): 3101–14. http://dx.doi.org/10.1109/tmm.2020.2969792.

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36

Cheng, Shuli, Liejun Wang, and Anyu Du. "Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval." Entropy 22, no. 11 (November 7, 2020): 1266. http://dx.doi.org/10.3390/e22111266.

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Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.

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37

Wang, Shaohua, Xiao Kang, Fasheng Liu, Xiushan Nie, and Xingbo Liu. "Discrete Two-Step Cross-Modal Hashing through the Exploitation of Pairwise Relations." Computational Intelligence and Neuroscience 2021 (September 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/4846043.

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The cross-modal hashing method can map heterogeneous multimodal data into a compact binary code that preserves semantic similarity, which can significantly enhance the convenience of cross-modal retrieval. However, the currently available supervised cross-modal hashing methods generally only factorize the label matrix and do not fully exploit the supervised information. Furthermore, these methods often only use one-directional mapping, which results in an unstable hash learning process. To address these problems, we propose a new supervised cross-modal hash learning method called Discrete Two-step Cross-modal Hashing (DTCH) through the exploitation of pairwise relations. Specifically, this method fully exploits the pairwise similarity relations contained in the supervision information: for the label matrix, the hash learning process is stabilized by combining matrix factorization and label regression; for the pairwise similarity matrix, a semirelaxed and semidiscrete strategy is adopted to potentially reduce the cumulative quantization errors while improving the retrieval efficiency and accuracy. The approach further combines an exploration of fine-grained features in the objective function with a novel out-of-sample extension strategy to enable the implicit preservation of consistency between the different modal distributions of samples and the pairwise similarity relations. The superiority of our method was verified through extensive experiments using two widely used datasets.

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Zhu, Xinghui, Liewu Cai, Zhuoyang Zou, and Lei Zhu. "Deep Multi-Semantic Fusion-Based Cross-Modal Hashing." Mathematics 10, no. 3 (January 29, 2022): 430. http://dx.doi.org/10.3390/math10030430.

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Due to the low costs of its storage and search, the cross-modal retrieval hashing method has received much research interest in the big data era. Due to the application of deep learning, the cross-modal representation capabilities have risen markedly. However, the existing deep hashing methods cannot consider multi-label semantic learning and cross-modal similarity learning simultaneously. That means potential semantic correlations among multimedia data are not fully excavated from multi-category labels, which also affects the original similarity preserving of cross-modal hash codes. To this end, this paper proposes deep multi-semantic fusion-based cross-modal hashing (DMSFH), which uses two deep neural networks to extract cross-modal features, and uses a multi-label semantic fusion method to improve cross-modal consistent semantic discrimination learning. Moreover, a graph regularization method is combined with inter-modal and intra-modal pairwise loss to preserve the nearest neighbor relationship between data in Hamming subspace. Thus, DMSFH not only retains semantic similarity between multi-modal data, but integrates multi-label information into modal learning as well. Extensive experimental results on two commonly used benchmark datasets show that our DMSFH is competitive with the state-of-the-art methods.

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Li, Mingyong, Longfei Ma, Yewen Li, and Mingyuan Ge. "CCAH: A CLIP-Based Cycle Alignment Hashing Method for Unsupervised Vision-Text Retrieval." International Journal of Intelligent Systems 2023 (February 23, 2023): 1–16. http://dx.doi.org/10.1155/2023/7992047.

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Due to the advantages of low storage cost and fast retrieval efficiency, deep hashing methods are widely used in cross-modal retrieval. Images are usually accompanied by corresponding text descriptions rather than labels. Therefore, unsupervised methods have been widely concerned. However, due to the modal divide and semantic differences, existing unsupervised methods cannot adequately bridge the modal differences, leading to suboptimal retrieval results. In this paper, we propose CLIP-based cycle alignment hashing for unsupervised vision-text retrieval (CCAH), which aims to exploit the semantic link between the original features of modalities and the reconstructed features. Firstly, we design a modal cyclic interaction method that aligns semantically within intramodality, where one modal feature reconstructs another modal feature, thus taking full account of the semantic similarity between intramodal and intermodal relationships. Secondly, introducing GAT into cross-modal retrieval tasks. We consider the influence of text neighbour nodes and add attention mechanisms to capture the global features of text modalities. Thirdly, Fine-grained extraction of image features using the CLIP visual coder. Finally, hash encoding is learned through hash functions. The experiments demonstrate on three widely used datasets that our proposed CCAH achieves satisfactory results in total retrieval accuracy. Our code can be found at: https://github.com/CQYIO/CCAH.git.

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Li, Tieying, Xiaochun Yang, Bin Wang, Chong Xi, Hanzhong Zheng, and Xiangmin Zhou. "Bi-CMR: Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10275–82. http://dx.doi.org/10.1609/aaai.v36i9.21268.

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Cross-modal hashing has attracted considerable attention for large-scale multimodal data. Recent supervised cross-modal hashing methods using multi-label networks utilize the semantics of multi-labels to enhance retrieval accuracy, where label hash codes are learned independently. However, all these methods assume that label annotations reliably reflect the relevance between their corresponding instances, which is not true in real applications. In this paper, we propose a novel framework called Bidirectional Reinforcement Guided Hashing for Effective Cross-Modal Retrieval (Bi-CMR), which exploits a bidirectional learning to relieve the negative impact of this assumption. Specifically, in the forward learning procedure, we highlight the representative labels and learn the reinforced multi-label hash codes by intra-modal semantic information, and further adjust similarity matrix. In the backward learning procedure, the reinforced multi-label hash codes and adjusted similarity matrix are used to guide the matching of instances. We construct two datasets with explicit relevance labels that reflect the semantic relevance of instance pairs based on two benchmark datasets. The Bi-CMR is evaluated by conducting extensive experiments over these two datasets. Experimental results prove the superiority of Bi-CMR over four state-of-the-art methods in terms of effectiveness.

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Zhang, Xuewang, Jinzhao Lin, and Yin Zhou. "DHLBT: Efficient Cross-Modal Hashing Retrieval Method Based on Deep Learning Using Large Batch Training." International Journal of Software Engineering and Knowledge Engineering 31, no. 07 (July 2021): 949–71. http://dx.doi.org/10.1142/s0218194021500297.

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Cross-modal hashing has attracted considerable attention as it can implement rapid cross-modal retrieval through mapping data of different modalities into a common Hamming space. With the development of deep learning, more and more cross-modal hashing methods based on deep learning are proposed. However, most of these methods use a small batch to train a model. The large batch training can get better gradients and can improve training efficiency. In this paper, we propose the DHLBT method, which uses the large batch training and introduces orthogonal regularization to improve the generalization ability of the DHLBT model. Moreover, we consider the discreteness of hash codes and add the distance between hash codes and features to the objective function. Extensive experiments on three benchmarks show that our method achieves better performance than several existing hashing methods.

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Shi, Ge, Feng Li, Lifang Wu, and Yukun Chen. "Object-Level Visual-Text Correlation Graph Hashing for Unsupervised Cross-Modal Retrieval." Sensors 22, no. 8 (April 11, 2022): 2921. http://dx.doi.org/10.3390/s22082921.

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The core of cross-modal hashing methods is to map high dimensional features into binary hash codes, which can then efficiently utilize the Hamming distance metric to enhance retrieval efficiency. Recent development emphasizes the advantages of the unsupervised cross-modal hashing technique, since it only relies on relevant information of the paired data, making it more applicable to real-world applications. However, two problems, that is intro-modality correlation and inter-modality correlation, still have not been fully considered. Intra-modality correlation describes the complex overall concept of a single modality and provides semantic relevance for retrieval tasks, while inter-modality correction refers to the relationship between different modalities. From our observation and hypothesis, the dependency relationship within the modality and between different modalities can be constructed at the object level, which can further improve cross-modal hashing retrieval accuracy. To this end, we propose a Visual-textful Correlation Graph Hashing (OVCGH) approach to mine the fine-grained object-level similarity in cross-modal data while suppressing noise interference. Specifically, a novel intra-modality correlation graph is designed to learn graph-level representations of different modalities, obtaining the dependency relationship of the image region to image region and the tag to tag in an unsupervised manner. Then, we design a visual-text dependency building module that can capture correlation semantic information between different modalities by modeling the dependency relationship between image object region and text tag. Extensive experiments on two widely used datasets verify the effectiveness of our proposed approach.

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Zhang, Donglin, Xiao-Jun Wu, and Jun Yu. "Label Consistent Flexible Matrix Factorization Hashing for Efficient Cross-modal Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3 (July 22, 2021): 1–18. http://dx.doi.org/10.1145/3446774.

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Hashing methods have sparked a great revolution on large-scale cross-media search due to its effectiveness and efficiency. Most existing approaches learn unified hash representation in a common Hamming space to represent all multimodal data. However, the unified hash codes may not characterize the cross-modal data discriminatively, because the data may vary greatly due to its different dimensionalities, physical properties, and statistical information. In addition, most existing supervised cross-modal algorithms preserve the similarity relationship by constructing an n × n pairwise similarity matrix, which requires a large amount of calculation and loses the category information. To mitigate these issues, a novel cross-media hashing approach is proposed in this article, dubbed label flexible matrix factorization hashing (LFMH). Specifically, LFMH jointly learns the modality-specific latent subspace with similar semantic by the flexible matrix factorization. In addition, LFMH guides the hash learning by utilizing the semantic labels directly instead of the large n × n pairwise similarity matrix. LFMH transforms the heterogeneous data into modality-specific latent semantic representation. Therefore, we can obtain the hash codes by quantifying the representations, and the learned hash codes are consistent with the supervised labels of multimodal data. Then, we can obtain the similar binary codes of the corresponding modality, and the binary codes can characterize such samples flexibly. Accordingly, the derived hash codes have more discriminative power for single-modal and cross-modal retrieval tasks. Extensive experiments on eight different databases demonstrate that our model outperforms some competitive approaches.

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Zhu, Liping, Gangyi Tian, Bingyao Wang, Wenjie Wang, Di Zhang, and Chengyang Li. "Multi-attention based semantic deep hashing for cross-modal retrieval." Applied Intelligence 51, no. 8 (January 20, 2021): 5927–39. http://dx.doi.org/10.1007/s10489-020-02137-w.

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Liu, Yun, Shujuan Ji, Qiang Fu, Dickson K. W. Chiu, and Maoguo Gong. "An efficient dual semantic preserving hashing for cross-modal retrieval." Neurocomputing 492 (July 2022): 264–77. http://dx.doi.org/10.1016/j.neucom.2022.04.011.

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Wu, Jiagao, Weiwei Weng, Junxia Fu, Linfeng Liu, and Bin Hu. "Deep semantic hashing with dual attention for cross-modal retrieval." Neural Computing and Applications 34, no. 7 (November 12, 2021): 5397–416. http://dx.doi.org/10.1007/s00521-021-06696-y.

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Xie, De, Cheng Deng, Chao Li, Xianglong Liu, and Dacheng Tao. "Multi-Task Consistency-Preserving Adversarial Hashing for Cross-Modal Retrieval." IEEE Transactions on Image Processing 29 (2020): 3626–37. http://dx.doi.org/10.1109/tip.2020.2963957.

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Meng, Min, Haitao Wang, Jun Yu, Hui Chen, and Jigang Wu. "Asymmetric Supervised Consistent and Specific Hashing for Cross-Modal Retrieval." IEEE Transactions on Image Processing 30 (2021): 986–1000. http://dx.doi.org/10.1109/tip.2020.3038365.

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Su, Ruoqi, Di Wang, Zhen Huang, Yuan Liu, and Yaqiang An. "Online Adaptive Supervised Hashing for Large-Scale Cross-Modal Retrieval." IEEE Access 8 (2020): 206360–70. http://dx.doi.org/10.1109/access.2020.3037968.

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Xie, Liang, Lei Zhu, Peng Pan, and Yansheng Lu. "Cross-Modal Self-Taught Hashing for large-scale image retrieval." Signal Processing 124 (July 2016): 81–92. http://dx.doi.org/10.1016/j.sigpro.2015.10.010.

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