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research-article

Deep learning for visual understanding

Authors:

Michael S. LewAuthors Info & Claims

Neurocomputing, Volume 187, Issue C

Pages 27 - 48

https://doi.org/10.1016/j.neucom.2015.09.116

Published: 26 April 2016 Publication History

Abstract

Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the state-of-the-art in deep learning algorithms in computer vision by highlighting the contributions and challenges from over 210 recent research papers. It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks, such as image classification, object detection, image retrieval, semantic segmentation and human pose estimation. Finally, the paper summarizes the future trends and challenges in designing and training deep neural networks.

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cover image Neurocomputing

Neurocomputing Volume 187, Issue C

April 2016

133 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 26 April 2016

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Farhadloo MSharma AShekhar SMarkovic S(2024)Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR VisionACM Transactions on Spatial Algorithms and Systems10.1145/367920110:3(1-36)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3679201
Wang XYi XYu HHuang CPeng L(2024)Parallel Optimization for Accelerating the Generation of Correctly Rounded Elementary FunctionsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673125(21-31)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673125
Zhu RGuo DQi DChu ZYu XLi S(2024)A Survey of Trustworthy Representation Learning Across DomainsACM Transactions on Knowledge Discovery from Data10.1145/365730118:7(1-53)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3657301
Nguyen KNguyen HLe TThai VTran BLe LQuach L(2024)Tiny-CNN: Structuring Convolutional Neural Networks for Accurate Classification of Rice Leaf Diseases in Resource-Constrained EnvironmentsProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654570(171-178)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654570
Urettini EAtzeni DRamjattan RCarta ASerra ESpezzano F(2024)GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679822(2282-2291)Online publication date: 21-Oct-2024
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Hwang SLee WPark JSuh D(2024)First Realization of Batch Normalization in Flash-Based Binary Neural Networks Using a Single Voltage ShifterIEEE Transactions on Nanotechnology10.1109/TNANO.2024.346612823(677-683)Online publication date: 1-Jan-2024
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Yao JHan LYang KGuo GLiu NHuang XZheng ZZhang DHan J(2024)Contextual Dependency Vision Transformer for spectrogram-based multivariate time series analysisNeurocomputing10.1016/j.neucom.2023.127215572:COnline publication date: 1-Mar-2024
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Wei XFeng THuang QChen QZuo CMa H(2024)Deep learning-powered biomedical photoacoustic imagingNeurocomputing10.1016/j.neucom.2023.127207573:COnline publication date: 16-May-2024
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Mi JJiang XLuo LGao Y(2024)Toward explainable artificial intelligenceNeurocomputing10.1016/j.neucom.2023.126919563:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.126919
Wang QSun JHu YJiang WZhang XWang Z(2024)Deep learning-based flatness prediction via multivariate industrial data for steel strip during tandem cold rollingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121777237:PCOnline publication date: 1-Mar-2024
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