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Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval [article]

Fariborz Taherkhani, Veeru Talreja, Matthew C. Valenti, Nasser M. Nasrabadi
2020 arXiv   pre-print
The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to  ...  Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can beutilized for fast and flexible retrieval across different modalities.  ...  To summarize, the main contributions of this paper include: 1: Attribute guided deep cross-modal hashing (ADCMH): We utilize deep cross-modal hashing based on a margin-based DLL for face image retrieval  ... 
arXiv:2004.03378v1 fatcat:n5k5l74kxngcdm646wzhfncrxi

Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval [article]

Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
2019 arXiv   pre-print
In this paper, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant  ...  Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance.  ...  This is the first time where error correcting codes have been combined with deep cross modal hashing for image retrieval.  ... 
arXiv:1902.04139v1 fatcat:ychlkoe6yvaqfppi4xin5afysy

2020 Index IEEE Transactions on Biometrics, Behavior, and Identity Science Vol. 2

2020 IEEE Transactions on Biometrics Behavior and Identity Science  
., +, TBIOM Jan. 2020 15-25 Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval.  ...  ., +, TBIOM Jan. 2020 26-39 Image retrieval Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval.  ... 
doi:10.1109/tbiom.2020.3028623 fatcat:3vyvmzuhpffbpjngto3syzr3zq

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., +, TMM Dec. 2020 3075-3087 Multi-Level Correlation Adversarial Hashing for Cross-Modal Retrieval.  ...  ., +, TMM April 2020 874-884 Data handling Multi-Pathway Generative Adversarial Hashing for Unsupervised Cross- Modal Retrieval.  ...  Image watermarking Blind Watermarking for 3-D Printed Objects by Locally Modifying Layer Thickness. 2780 -2791 Low-Light Image Enhancement With Semi-Decoupled Decomposition.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval [article]

Xin Liu, Zhikai Hu, Haibin Ling, Yiu-ming Cheung
2018 arXiv   pre-print
As a result, the derived hash codes are more semantically meaningful for various challenging cross-modal retrieval tasks.  ...  Hashing has recently sparked a great revolution in cross-modal retrieval due to its low storage cost and high query speed.  ...  These three datasets consist of both image and text modalities, which are frequently utilized for cross-modal retrieval evaluation.  ... 
arXiv:1805.01963v1 fatcat:oooqywtqo5honeaccssqaekb3q

A Survey of Multi-View Representation Learning [article]

Yingming Li, Ming Yang, Zhongfei Zhang
2017 arXiv   pre-print
sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks, and multi-modal recurrent neural  ...  Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment.  ...  [145] propose a hashing-based model, called cross-modal similarity sensitive hashing (CMSSH), which approaches the cross-modality similarity learning problem by embedding the multi-modal data into a  ... 
arXiv:1610.01206v4 fatcat:xsi7ufxnlbdk5lz6ykrsnexfvm

Multimodal Machine Learning: A Survey and Taxonomy [article]

Tadas Baltrušaitis, Chaitanya Ahuja, Louis-Philippe Morency
2017 arXiv   pre-print
Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities.  ...  This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.  ...  The idea of cross-modal hashing is to create such codes for cross-modal retrieval [27] , [93] , [113] .  ... 
arXiv:1705.09406v2 fatcat:262fo4sihffvxecg4nwsifoddm

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 progress of deep learning has immensely benefited free-hand sketch research and applications.  ...  These are mainly used for cross-modal retrieval/matching, or cross-modal generation/synthesis.  ... 
arXiv:2001.02600v3 fatcat:lek5sivzsrat3i52lqh2eifnia

Deep multimodal representation learning: a survey

Wenzhong Guo, Jianwen Wang, Shiping Wanga
2019 IEEE Access  
Due to the powerful representation ability with multiple levels of abstraction, deep learning-based multimodal representation learning has attracted much attention in recent years.  ...  Finally, we suggest some important directions for future work.  ...  [32] , which aims to map sentences and images into a common space for cross-modal retrieval.  ... 
doi:10.1109/access.2019.2916887 fatcat:ms4wcgl5rncsbiywz27uss4ysq

Online multimodal deep similarity learning with application to image retrieval

Pengcheng Wu, Steven C.H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
2013 Proceedings of the 21st ACM international conference on Multimedia - MM '13  
We conduct an extensive set of experiments to evaluate the performance of the proposed algorithms for multimodal image retrieval tasks, in which the encouraging results validate the effectiveness of the  ...  Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks.  ...  Figure 1 : 1 Overview of the proposed multimodal deep similarity learning scheme for image retrieval many of them usually follow the principle of large margin learning.  ... 
doi:10.1145/2502081.2502112 dblp:conf/mm/WuHXZWM13 fatcat:vudnz4cckzgjhm6xzzw6xc5lu4

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

2020 IEEE transactions on circuits and systems for video technology (Print)  
., +, TCSVT Oct. 2020 3788-3802 SCRATCH: A Scalable Discrete Matrix Factorization Hashing Framework for Cross-Modal Retrieval.  ...  ., +, TCSVT April 2020 1162-1172 SCRATCH: A Scalable Discrete Matrix Factorization Hashing Framework for Cross-Modal Retrieval. Chen, Z., +, TCSVT July 2020 2262-2275 Bit rate Deep Video Precoding.  ...  A Memory-Efficient Hardware Architecture for Connected Component Labeling in Embedded System.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Fashion Meets Computer Vision: A Survey [article]

Wen-Huang Cheng, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati, Jiaying Liu
2021 arXiv   pre-print
For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.  ...  landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer,  ...  It thus attracts tremendous attention from many research communities to develop cross-scenario image-based fashion retrieval tasks for matching the real-world fashion items to the online shopping image  ... 
arXiv:2003.13988v2 fatcat:ajzvyn4ck5gqxk5ht5u3mrdmba

Table of Contents

2020 2020 IEEE International Conference on Image Processing (ICIP)  
................. 2291 CROSS-MODAL RETRIEVAL 0LQJ\DQJ /L <DQJ /L 6KDR/XQ +XDQJ /LQ =KDQJ 7VLQJKXD 8QLYHUVLW\ &KLQD ARS-06.8: INFRARED-VISIBLE PERSON RE-IDENTIFICATION VIA CROSS-MODALITY ...............  ...  DQ %DL /LQJ<X 'XDQ 3HNLQJ 8QLYHUVLW\ &KLQD ARS-05.5: DEEP SELF-LEARNING HASHING FOR IMAGE RETRIEVAL .................................................................... 1556 -LDZHL =KDQ =KDRJXR 0R <XHVKHQJ  ...  RECOGNITION $EG (O 5DKPDQ 6KDED\HN 'MDPLOD $RXDGD 8QLYHUVLW\ RI /X[HPERXUJ /X[HPERXUJ .VHQL\D &KHUHQNRYD *OHE *XVHY $UWHF ' /X[HPERXUJ 3D-01.6: DEEP REGRESSION FOREST WITH SOFT- ATTENTION FOR HEAD  ... 
doi:10.1109/icip40778.2020.9191006 fatcat:3fkxl2sjmre2jkryewwo5mlahi

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, TPAMI April 2021 1445-1451 MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

Deep Learning: Methods and Applications

Li Deng
2014 Foundations and Trends® in Signal Processing  
Semantic hashing with deep autoencoders for document indexing and retrieval Here we discuss the "semantic hashing" approach for the application of deep autoencoders to document indexing and retrieval as  ...  This strategy has been applied to construct a deep autoencoder to map images to short binary code for fast, content-based image retrieval, to encode documents (called semantic hashing), and to encode spectrogram-like  ... 
doi:10.1561/2000000039 fatcat:vucffxhse5gfhgvt5zphgshjy4
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