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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
Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance.  ...  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  ...  We present a novel CMH framework called CMH-ECC for error-corrected attribute guided deep cross-modal hashing for face-image retrieval from large datasets.  ... 
arXiv:1902.04139v1 fatcat:ychlkoe6yvaqfppi4xin5afysy

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 goal of NECD network in DNDCMH isto error correct the hash codes generated by ADCMH to improve the retrieval efficiency.  ...  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  ...  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

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., and Lam, K  ...  ., +, TMM Jan. 2020 215-228 Error correction An Efficient NVoD Scheme Using Implicit Error Correction and Subchan- nels for Wireless Networks.  ...  ., +, TMM Dec. 2020 3075-3087 Multi-Level Correlation Adversarial Hashing for Cross-Modal Retrieval.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TMM 2021 3907-3918 Mask Cross-Modal Hashing Networks. Lin, Q., +, TMM 2021 550-558 Online Hashing With Bit Selection for Image Retrieval.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion [article]

Yang Wang
2020 arXiv   pre-print
In this paper, we provide a substantial overview of the existing state-of-the-arts on the filed of multi-modal data analytics from shallow to deep spaces.  ...  Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces.  ...  It jointly learned unified hash codes for cross-modal instances and a pair of hash functions for queries. Cao et al.  ... 
arXiv:2006.08159v1 fatcat:g4467zmutndglmy35n3eyfwxku

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 4254-4268 Multi-Task Consistency-Preserving Adversarial Hashing for Cross-Modal Retrieval.  ...  ., +, TIP 2020 5289-5300 Unsupervised Deep Cross-modality Spectral Hashing.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

A Natural and Immersive Virtual Interface for the Surgical Safety Checklist Training

Andrea Ferracani, Daniele Pezzatini, Alberto Del Bimbo
2014 Proceedings of the 2014 ACM International Workshop on Serious Games - SeriousGames '14  
Since the launch of Bing (www.bing.com) in June 2009, we have seen Bing web search market share in the US more than doubled and Bing image search query share quadrupled.  ...  With the focus on natural language and entity understanding, for instance, we have improved Bing's ability to understand the user intent beyond queries and keywords.  ...  for Manga Classification Supervised Hashing with Error Correcting Codes Pedestrian Attribute Classification at Far Distance MSVA: Musical Street View Animator: An Effective and Efficient Way to Enjoy  ... 
doi:10.1145/2656719.2656725 dblp:conf/mm/FerracaniPB14a fatcat:obsb2i4iybhu3dq77hujvjtbze

Greedy Learning of Deep Boltzmann Machine (GDBM)'s Variance and Search Algorithm for Efficient Image Retrieval

Mudhafar Jalil Jassim Ghrabat, Guangzhi Ma, Hong Liu, Zaid Ameen Abduljabbar, Mustafa A.Al Sibahee, Safa Jalil Jassim
2019 IEEE Access  
Finally, the relevant features are utilized for the greedy learning of deep Boltzmann machine classifier (GDBM).  ...  Initially, a preprocessing technique is introduced in this study, a technique that uses a median filter to remove noise to achieve improved accuracy and reliability.  ...  In addition to that, Mean Square Error loss and Cross-entropy loss are added respectively for improved feature-based learning and hash coding.  ... 
doi:10.1109/access.2019.2948266 fatcat:lzhyuujngvhehm54jlue2kfis4

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 Jan. 2020 217-231 Hu, X., see Zhu, L., TCSVT Oct. 2020 3358-3371 Hu, Y., Lu, M., Xie, C., and Lu, X  ...  ., and Zeng, B., MUcast: Linear Uncoded Multiuser TCSVT Nov. 2020 4299-4308 Hu, R., see Chen, L., TCSVT Dec. 2020 4513-4525 Hu, R., see Wang, X., TCSVT Nov. 2020 4309-4320 Hu, X., see Zhang, X  ...  ., +, 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

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
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  ...  (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions for the community.  ...  These are mainly used for cross-modal retrieval/matching, or cross-modal generation/synthesis.  ... 
arXiv:2001.02600v3 fatcat:lek5sivzsrat3i52lqh2eifnia

2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42

2021 IEEE Transactions on Pattern Analysis and Machine Intelligence  
., and Nishino, K., Recognizing Material Properties from Images; 1981-1995 Sebe, N., see Pilzer, A., 2380-2395 Seddik, M., see Tamaazousti, Y., 2212-2224 Shah, M., see Kalayeh, M.M., TPAMI June 2020  ...  ., +, 2508- 2522 Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery.  ...  ., +, TPAMI Aug. 2020 1898-1912 Guided Attention Inference Network. Li, K., +, TPAMI Dec. 2020 2996-3010 Recomputation of the Dense Layers for Performance Improvement of DCNN.  ... 
doi:10.1109/tpami.2020.3036557 fatcat:3j6s2l53x5eqxnlsptsgbjeebe

Deep multimodal representation learning: a survey

Wenzhong Guo, Jianwen Wang, Shiping Wanga
2019 IEEE Access  
Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data.  ...  To facilitate the discussion on how the heterogeneity gap is narrowed, according to the underlying structures in which different modalities are integrated, we category deep multimodal representation learning  ...  Here, the hash codes play a key role for both generator and discriminator.  ... 
doi:10.1109/access.2019.2916887 fatcat:ms4wcgl5rncsbiywz27uss4ysq

Table of Contents

2020 2020 IEEE International Conference on Image Processing (ICIP)  
SENSING IMAGES /LEDR =KDQJ :DQQLQJ =KX <DQJ 6XQ %HLMLQJ 1RUPDO 8QLYHUVLW\ &KLQD COM-02: LOSSLESS AND CHANNEL CODING OF IMAGES & VIDEO COM-02.1: ROBUST H.264 VIDEO DECODING USING CRC-BASED SINGLE ERROR  ...  ................. 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 ...............  ...  CODED-APERTURE FOR UNSUPERVISED CLASSIFICATION OF HYPERSEPCTRAL IMAGERY -LDQFKHQ =KX 7RQJ =KDQJ 6KHQJMLH =KDR 7RQJML 8QLYHUVLW\ &KLQD IMT-02.4: ADMM-INSPIRED RECONSTRUCTION NETWORK FOR COMPRESSIVE  ... 
doi:10.1109/icip40778.2020.9191006 fatcat:3fkxl2sjmre2jkryewwo5mlahi

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
., +, LSP 2020 16-20 Binary codes Correlation Filtering-Based Hashing for Fine-Grained Image Retrieval. Ma, L., +, LSP 2020 2129-2133 Efficient Parameter-Free Adaptive Multi-Modal Hashing.  ...  ., +, LSP 2020 1270-1274 Graph Regularized Deep Discrete Hashing for Multi-Label Image Retrieval.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Fashion Meets Computer Vision: A Survey [article]

Wen-Huang Cheng, Sijie Song, Chieh-Yun Chen, Shintami Chusnul Hidayati, Jiaying Liu
2021 arXiv   pre-print
Fashion is the way we present ourselves to the world and has become one of the world's largest industries.  ...  landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer,  ...  [103] presented a hierarchical super-pixel fusion algorithm for obtaining the intact query clothing item and used sparse coding for improving accuracy.  ... 
arXiv:2003.13988v2 fatcat:ajzvyn4ck5gqxk5ht5u3mrdmba
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