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A Review on Methods and Applications in Multimodal Deep Learning [article]

Jabeen Summaira, Xi Li, Amin Muhammad Shoib, Jabbar Abdul
2022 arXiv   pre-print
A fine-grained taxonomy of various multimodal deep learning methods is proposed, elaborating on different applications in more depth.  ...  Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years.  ...  MMDL Methods Multimodal MMDL Methods and Applications: Various methods and applications are designed using multimodal deep learning techniques.  ... 
arXiv:2202.09195v1 fatcat:wwxrmrwmerfabbenleylwmmj7y

Multimodal Emotion Recognition using Deep Learning

Sharmeen M.Saleem Abdullah Abdullah, Siddeeq Y. Ameen Ameen, Mohammed Mohammed sadeeq, Subhi Zeebaree
2021 Journal of Applied Science and Technology Trends  
This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies.  ...  This would make it possible for people to survive and be used in widespread fields, including education and medicine.  ...  In the proposed multimodal emotion recognition and after reviewing previous studies, everyone used more than one modal to identify emotions using different methods and techniques combined with deep learning  ... 
doi:10.38094/jastt20291 fatcat:2ofkuynxebgb5glhsaii5zcq4u

Deep Vision Multimodal Learning: Methodology, Benchmark, and Trend

Wenhao Chai, Gaoang Wang
2022 Applied Sciences  
Several applications and benchmarks on vision tasks are listed to help researchers gain a deeper understanding of progress in the field.  ...  This paper reviews the types of architectures used in multimodal learning, including feature extraction, modality aggregation, and multimodal loss functions.  ...  These new tasks focus on exploring deep multimodal learning for practical applications.  ... 
doi:10.3390/app12136588 fatcat:bokdxwkcwbgjlpblfrwbj4mtxm

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Khaled Bayoudh, Raja Knani, Fayçal Hamdaoui, Abdellatif Mtibaa
2021 The Visual Computer  
In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based  ...  We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains.  ...  In addition to surveys of recent advances in deep multimodal learning itself, we also discussed the main methods of multimodal fusion and reviewed the latest advanced applications and multimodal datasets  ... 
doi:10.1007/s00371-021-02166-7 pmid:34131356 pmcid:PMC8192112 fatcat:jojwyc6slnevzk7eaiutlmlgfe

Multimodal Intelligence: Representation Learning, Information Fusion, and Applications [article]

Chao Zhang, Zichao Yang, Xiaodong He, Li Deng
2020 arXiv   pre-print
In this paper, we provide a technical review of available models and learning methods for multimodal intelligence.  ...  This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and  ...  ACKNOWLEDGEMENT The authors are grateful to the editor and anonymous reviewers for their valuable suggestions that helped to make this paper better.  ... 
arXiv:1911.03977v3 fatcat:ojazuw3qzvfqrdweul6qdpxuo4

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

Wei Chen, Weiping Wang, Li Liu, Michael S. Lew
2020 Neurocomputing  
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.  ...  Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures  ...  Recent reviews [177, 178] have reported comprehensive introductions to GCNs. However, we focus on recent ideas and processes in deep multimodal learning.  ... 
doi:10.1016/j.neucom.2020.10.042 fatcat:hyjkj5enozfrvgzxy6avtbmoxu

A Special Section on Deep & Advanced Machine Learning Approaches for Human Behavior Analysis

Yizhang Jiang, Kim-Kwang Raymond Choo, Hoon Ko
2021 Journal of Information Processing Systems  
Given the constant advances in machine and deep learning methods, such as deep learning, transfer learning, reinforcement learning, and federated learning, we can also utilize such techniques in cognitive  ...  , and skin conductance) for different activities and studies, such as using the data to train machine-/deep-learning models in order to facilitate medical diagnosis and other decision-making.  ...  In this thematic issue, we seek to provide a forum for researchers from cognitive computing and machine learning to present recent progress in deep and advanced machine learning research with applications  ... 
doi:10.3745/jips.01.0074 dblp:journals/jips/JiangCK21 fatcat:lexijhdgkfaizauhi54h7debqu

Deep learning: from speech recognition to language and multimodal processing

Li Deng
2016 APSIPA Transactions on Signal and Information Processing  
Next, more challenging applications of deep learning, natural language and multimodal processing, are selectively reviewed and analyzed.  ...  reviews of earlier studies on (shallow) neural networks and on (deep) generative models relevant to the introduction of deep neural networks (DNN) to speech recognition several years ago.  ...  B) A selected review on deep learning for multimodal processing Multimodal processing is a class of applications closely related to multitask learning, where the learning domains or "tasks" cut across  ... 
doi:10.1017/atsip.2015.22 fatcat:rsaafhsbfzeo3l6dxycjewcmi4

1st MICCAI workshop on deep learning in medical image analysis

Gustavo Carneiro, João Manuel R. S. Tavares, Andrew P. Bradley, João Paulo Papa, Jacinto C. Nascimento, Jaime S. Cardoso, Zhi Lu, Vasileios Belagiannis
2018 Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization  
1st MICCAI workshop on deep learning in medical image analysis Deep learning methods are quickly receiving a great deal of attention by the machine learning and computer vision communities for several  ...  and Computer Assisted Intervention (MICCAI) dedicated to the presentation of works focused on the design and use of deep learning methods in medical image analysis applications.  ... 
doi:10.1080/21681163.2018.1457242 fatcat:ehch2lmp6vfmlgnmhutyyxcdwi

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Shih-Cheng Huang, Anuj Pareek, Saeed Seyyedi, Imon Banerjee, Matthew P. Lungren
2020 npj Digital Medicine  
We conducted a systematic search on PubMed and Scopus for original research articles leveraging deep learning for fusion of multimodality data.  ...  By means of this systematic review, we present current knowledge, summarize important results and provide implementation guidelines to serve as a reference for researchers interested in the application  ...  The research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Number R01LM012966.  ... 
doi:10.1038/s41746-020-00341-z pmid:33083571 pmcid:PMC7567861 fatcat:vo7os2ial5eqzju3ere3x2mgwi

Introduction to the Special Issue on Deep Learning for Multi-Modal Intelligence Across Speech, Language, Vision, and Heterogeneous Signals

Xiaodong He, Li Deng, Richard Rose, Minlie Huang, Isabel Trancoso, Chao Zhang
2020 IEEE Journal on Selected Topics in Signal Processing  
The issue begins with a review article, "Multimodal Intelligence: Representation Learning, Information Fusion, and Applications" by Zhang et al., which presents a comprehensive analysis of some recent  ...  This special issue brings together a diverse but complementary set of contributions on emerging deep learning methods for problems based on multiple modalities including speech, text, image and video.  ... 
doi:10.1109/jstsp.2020.2989852 fatcat:xlcv7jzkbvfirbbmrh4fpmtyxm

Deep Learning in Biological Image and Signal Processing [From the Guest Editors]

Erik Meijering, Vince D. Calhoun, Gloria Menegaz, David J. Miller, Jong Chul Ye
2022 IEEE Signal Processing Magazine  
Recently, a major paradigm shift has taken place with the widespread adoption and application of deep learning technologies, which are now rap-  ...  For a long time, the primary modus operandi in developing such methods has been to hand-design mathematical models of underlying phenomena and translate them into computational algorithms.  ...  Yet many scientific and engineering difficulties remain to further improve the performance of deep learning methods and make them reliable enough for critical tasks in biological research applications.  ... 
doi:10.1109/msp.2021.3134525 fatcat:ux3vcvteurdx7bhoqujnf5spwa

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Taeho Jo, Kwangsik Nho, Andrew J. Saykin
2019 Frontiers in Aging Neuroscience  
A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018.  ...  A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed.  ...  MATERIALS AND METHODS We conducted a systematic review on previous studies that used deep learning approaches for diagnostic classification of AD with multimodal neuroimaging data.  ... 
doi:10.3389/fnagi.2019.00220 pmid:31481890 pmcid:PMC6710444 fatcat:udknjrow3rf5fkr7bkjcswy3jy

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
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.  ...  Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures  ...  Acknowledgments This work was supported by LIACS MediaLab at Leiden University and China Scholarship Council (CSC No. 201703170183). We appreciate the helpful editing work from Dr. Erwin Bakker.  ... 
arXiv:2010.08189v1 fatcat:2l7molbcn5hf3oyhe3l52tdwra

A Review on Explainability in Multimodal Deep Neural Nets

Gargi Joshi, Rahee Walambe, Ketan Kotecha
2021 IEEE Access  
This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks  ...  Several multimodal fusion methods employing deep learning models are proposed in the literature.  ...  and applications in the integrated space of deep multimodal learning [1] .  ... 
doi:10.1109/access.2021.3070212 fatcat:5wtxr4nf7rbshk5zx7lzbtcram
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