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Deep semantic cross modal hashing based on graph similarity of modal-specific

Junzheng Li
2021 IEEE Access  
A Scalable disCRete mATrix faCtorization Hashing (SCRATCH) [36] for Cross Modal Retrieval generates the hash codes by the latent representations which are learned by utilizing Collective Matrix Factorization  ...  Supervised Matrix Factorization Hashing for Cross-Modal Retrieval (SMFH) [19] is a supervised cross modal hashing method based on collective matrix factorization, which considers both the label consistency  ... 
doi:10.1109/access.2021.3093357 fatcat:uyouxawgzbhzhlrsufj4iauiuy

Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval [article]

Kaiyi Luo, Chao Zhang, Huaxiong Li, Xiuyi Jia, Chunlin Chen
2022 arXiv   pre-print
Previous literatures have achieved promising results for Cross-Modal Retrieval (CMR) by discovering discriminative hash codes and modality-specific hash functions.  ...  In recent years, Cross-Modal Hashing (CMH) has aroused much attention due to its fast query speed and efficient storage.  ...  Typical works include Supervised Matrix Factorization Hashing (SMFH) [9] , Discrete Cross-modal Hashing (DCH) [19] , Scalable disCRete mATrix faCtorization Hashing (SCRATCH) [2] and Subspace Relation  ... 
arXiv:2207.11880v1 fatcat:4gfa7xlb2feqrejqet2fqzy2oe

MOON: Multi-Hash Codes Joint Learning for Cross-Media Retrieval

Donglin Zhang, Xiao-Jun Wu, He-Feng Yin, Josef Kittler
2021 Pattern Recognition Letters  
To this end, we develop a novel Multiple hash cOdes jOint learNing method (MOON) for cross-media retrieval.  ...  Therefore, these models need to be retrained when the hash length changes, that consumes additional computation power, reducing the scalability in practical applications. 2) Existing cross-modal approaches  ...  Scalable matrix factorization hashing (SCARATCH) (Li et al., 2018b) , which learns a latent semantic subspace by adopting a matrix factorization scheme and generates hash codes discretely.  ... 
doi:10.1016/j.patrec.2021.07.018 fatcat:rtoisvhcxnav3jgbnk6alwqk5m

A probabilistic model for multimodal hash function learning

Yi Zhen, Dit-Yan Yeung
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
Given data from multiple modalities, we devise an efficient algorithm for the learning of binary latent factors which corresponds to hash function learning.  ...  MLBE regards the binary latent factors as hash codes in a common Hamming space.  ...  We compare MLBE with CMSSH 4 and CVH 5 on two common cross-modal retrieval tasks.  ... 
doi:10.1145/2339530.2339678 dblp:conf/kdd/ZhenY12 fatcat:dipo4jvivncdppgyuxehutwhtu

Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval

Jun Long, Longzhi Sun, Liujie Hua, Zhan Yang
2021 Applied Sciences  
Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications.  ...  of matrix to solve the problem of the inevitable noise and subjective factors in labels.  ...  Scalable Discrete Matrix Factorization Hashing (SCRATCH) [38] is a two-step hashing method, which first generates the hash codes, and then learns the hash functions based on the learned hash codes.  ... 
doi:10.3390/app11188769 fatcat:cp2bnipljjcevm4e26lfzvijsy

Adaptive Hashing With Sparse Matrix Factorization

Huawen Liu, Xuelong Li, Shichao Zhang, Qi Tian
2019 IEEE Transactions on Neural Networks and Learning Systems  
One of the most appealing techniques for hashing learning is matrix factorization.  ...  In addition, parameter tuning is always a challenging and head-scratching problem for sparse hashing learning.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous referees and the associate editor for their valuable comments and suggestions and their help in significantly improving this article.  ... 
doi:10.1109/tnnls.2019.2954856 pmid:31899436 fatcat:dr3bdltotzc6plyn4d77xjkhba

Large-scale retrieval for medical image analytics: A comprehensive review

Zhongyu Li, Xiaofan Zhang, Henning Müller, Shaoting Zhang
2018 Medical Image Analysis  
Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale.  ...  In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval.  ...  Then, it learns 674 the hashing function (i.e. a rotation matrix) by minimizing the binarization 675 error between the new feature matrix and the corresponding binarized fea-676 ture matrix (Gong and Lazebnik  ... 
doi:10.1016/j.media.2017.09.007 pmid:29031831 fatcat:s6jnxawnongufgdngpjeifv3vm

Deep Learning for Free-Hand Sketch: A Survey [article]

Peng Xu, Timothy M. Hospedales, Qiyue Yin, Yi-Zhe Song, Tao Xiang, Liang Wang
2022 arXiv   pre-print
(iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.  ...  The main contents of this survey include: (i) A discussion of the intrinsic traits and unique challenges of free-hand sketch, to highlight the essential differences between sketch data and other data modalities  ...  These are mainly used for cross-modal retrieval/matching, or cross-modal generation/synthesis.  ... 
arXiv:2001.02600v3 fatcat:lek5sivzsrat3i52lqh2eifnia

New Ideas and Trends in Deep Multimodal Content Understanding: A Review

Wei Chen, Weiping Wang, Li Liu, Michael S. Lew
2020 Neurocomputing  
These models go beyond the simple image classifiers in which they can do uni-directional (e.g. image captioning, image generation) and bi-directional (e.g. cross-modal retrieval, visual question answering  ...  Finally, we include several promising directions for future research.  ...  [84] introduce an efficient discrete optimal scheme for binary code learning in which a hash codes matrix is construct. Focusing on the feature quantization, Wang et al.  ... 
doi:10.1016/j.neucom.2020.10.042 fatcat:hyjkj5enozfrvgzxy6avtbmoxu

New Ideas and Trends in Deep Multimodal Content Understanding: A Review [article]

Wei Chen and Weiping Wang and Li Liu and Michael S. Lew
2020 arXiv   pre-print
These models go beyond the simple image classifiers in which they can do uni-directional (e.g. image captioning, image generation) and bi-directional (e.g. cross-modal retrieval, visual question answering  ...  Finally, we include several promising directions for future research.  ...  1) Cross-modal retrieval Single-modal and cross-modal retrieval have been researched for decades [61] .  ... 
arXiv:2010.08189v1 fatcat:2l7molbcn5hf3oyhe3l52tdwra

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
., see Sepas-Moghaddam, A., TCSVT Dec. 2020 4496-4512 Hassanpour, H., see Khosravi, M.H., TCSVT Jan. 2020 48-58 Hatzinakos, D., see 2900-2916 Hayat, M., see 2900-2916 He, C., Hu, Y., Chen, Y., Fan  ...  Lai, H., +, TCSVT April 2020 1162-1172 SCRATCH: A Scalable Discrete Matrix Factorization Hashing Framework for Cross-Modal Retrieval.  ...  ., +, TCSVT Oct. 2020 3788-3802 SCRATCH: A Scalable Discrete Matrix Factorization Hashing Framework for Cross-Modal Retrieval.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Learning to Hash for Indexing Big Data - A Survey [article]

Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang
2015 arXiv   pre-print
As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged.  ...  In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem.  ...  To represent such a discrete ranking list, a ranking triplet matrix S P R NˆN is defined as Spq m ; x i , x j q " $ & % 1 : r q i ă r q j 1 : r q i ą r q j 0 : r q i " r q j . (30) Hence for a set of query  ... 
arXiv:1509.05472v1 fatcat:haj52w3cbbgszlmalfyu2kvzde

Learning to Hash for Indexing Big Data—A Survey

Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang
2016 Proceedings of the IEEE  
As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged.  ...  In many critical applications such as largescale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem.  ...  In addition, this emerging hash learning framework can be exploited for some general machine learning and data mining tasks, including cross-modality data fusion [139] , large-scale optimization [140  ... 
doi:10.1109/jproc.2015.2487976 fatcat:4eok2ubzxnc5nmc4hgt4qmqhcy

Discrete Two-Step Cross-Modal Hashing through the Exploitation of Pairwise Relations

Shaohua Wang, Xiao Kang, Fasheng Liu, Xiushan Nie, Xingbo Liu
2021
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.  ...  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  ...  [17] proposed a scalable cross-modal hashing method in which matrix factorization is applied to the cross-modal field.  ... 
doi:10.1155/2021/4846043 pmid:34616443 pmcid:PMC8490049 fatcat:qdfrbgv2ybbu3jied7gofivevm

Transformers in Vision: A Survey [article]

Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah
2021 arXiv   pre-print
Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability  ...  Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions.  ...  We would also like to thank Mohamed Afham for his help with a figure.  ... 
arXiv:2101.01169v4 fatcat:ynsnfuuaize37jlvhsdki54cy4
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