Deep multi-view learning methods: A review

Yan, Xiaoqiang; Hu, Shizhe; Mao, Yiqiao; Ye, Yangdong; Yu, Hui

doi:10.1016/j.neucom.2021.03.090

Cited by 155 publications

(53 citation statements)

References 175 publications

(301 reference statements)

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“…The optimization problem (2) can be extended to the multi-view scenario, where different views of the data are reflected by different types of features. Multi-view learning provides a mechanism for exploiting multiple types of features [40] , [41] , [42] . Let

denote a data matrix from the

-th view, where

is the feature space dimension for the

-th view.…”

Section: Methodsmentioning

confidence: 99%

Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images

Hosseinzadeh

2022

Biomedical Signal Processing and Control

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denote a data matrix from the

-th view, where

is the feature space dimension for the

-th view.…”

Section: Methodsmentioning

confidence: 99%

Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images

Hosseinzadeh

2022

Biomedical Signal Processing and Control

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“…Apart from that, the authors also listed out the YOLO series of detectors -YOLOV2. Yan et al [11] reported a review on deep multi-view learning from videos focusing on representational deep learning methods such as conventional neural networks, deep brief networks, and multi-view auto-encoders.…”

Section: A Literature Surveymentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

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“…Cur- rently, model learning and prediction fusion are separate. A future direction is to combine deep multi-view learning [58] into the training session, which leads to an end-toend multi-view visibility prediction framework. Another direction is integrating edge computing [1,14] to construct a more efficient and robust visibility monitoring framework.…”

Section: Limitation and Future Workmentioning

confidence: 99%

Visibility estimation via deep label distribution learning in cloud environment

Song

Liu

et al. 2021

J Cloud Comp

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The visibility estimation of the environment has great research and application value in the fields of production. To estimate the visibility, we can utilize the camera to obtain some images as evidence. However, the camera only solves the image acquisition problem, and the analysis of image visibility requires strong computational power. To realize effective and efficient visibility estimation, we employ the cloud computing technique to realize high-through image analysis. Our method combines cloud computing and image-based visibility estimation into a powerful and efficient monitoring framework. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. The estimation result can be improved by fusing the predicting results of multiple images from different views. Our experiment shows that labeling the image with visibility distribution can boost the learning performance, and our method can obtain the visibility from the image efficiently.

show abstract

Deep multi-view learning methods: A review

Cited by 155 publications

References 175 publications

Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images

Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

Visibility estimation via deep label distribution learning in cloud environment

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