Uncertainty estimation for stereo matching based on evidential deep learning

Wang, Chen; Wang, Xiang; Zhang, Jiawei; Zhang, Liang; Bai, Xiangzhi; Ning, Xin; Zhou, Jun; Hancock, Edwin R.

doi:10.1016/j.patcog.2021.108498

Cited by 126 publications

(34 citation statements)

References 28 publications

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“…It is challenging to manually design and extract acoustic abstract features like musical instruments, rhythm, and harmony because different musical genres are frequently very different from one another. Deep learning [ 19 ] uses a nonlinear relationship created by connecting several hidden layers to automatically capture the target's high-level abstract features. Among them, CNN is currently a popular network model.…”

Section: Methodsmentioning

confidence: 99%

Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Lan

Fan²

2022

Journal of Environmental and Public Health

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In order to speed up the process of high-quality education and improve the level of education quality among the general public, people have pushed for the use of music art education in recent years. In this respect, this study covers the CNN-based assessment of the quality of music art teaching and creates a set of evaluation indices for that quality. The model architecture, network topology, learning parameters, and learning algorithm are all determined using this information, which also acts as the basis for the NN assessment model. The MATLAB simulation tool uses the CNN assessment model to train and learn a predetermined quantity of instructional quality data. The training experiment shows that this system can outperform other comparative systems in prediction accuracy by roughly 95%. Additionally, both the training and prediction accuracy of the model are completely acceptable. The evaluation findings and analytical data of the music art instructional quality assessment system created in this study can be used as a guide for determining the music art instructional quality and for making judgments regarding it.

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Section: Methodsmentioning

confidence: 99%

Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Lan

Fan²

2022

Journal of Environmental and Public Health

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“…is shows that the deep learning algorithm can bring a great improvement to the accuracy of radar gesture recognition. However, deep learning algorithms require a large amount of data and are prone to overfitting and error transfer for small datasets, resulting in poor recognition results [16].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Yuan

Zheng

2022

Mobile Information Systems

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With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.

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“…Jieba Chinese word segmentation component [10][11][12]is called, industrial Internet-related special words such as VR, AR, and smart Park are added into Jieba library, and precise mode is used to segment each document by default. In the process of word segmentation, function words and meaningless symbols are removed.…”

Section: Participlementioning

confidence: 99%

Discrete Hyperparameter Optimization Model Based on Skewed Distribution

2022

Mathematical Problems in Engineering

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As for the machine learning algorithm, one of the main factors restricting its further large-scale application is the value of hyperparameter. Therefore, researchers have done a lot of original numerical optimization algorithms to ensure the validity of hyperparameter selection. Based on previous studies, this study innovatively puts forward a model generated using skewed distribution (gamma distribution) as hyperparameter fitting and combines the Bayesian estimation method and Gauss hypergeometric function to propose a mathematically optimal solution for discrete hyperparameter selection. The results show that under strict mathematical conditions, the value of discrete hyperparameters can be given a reasonable expected value. This heuristic parameter adjustment method based on prior conditions can improve the accuracy of some traditional models in experiments and then improve the application value of models. At the same time, through the empirical study of relevant datasets, the effectiveness of the parameter adjustment strategy proposed in this study is further proved.

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Uncertainty estimation for stereo matching based on evidential deep learning

Cited by 126 publications

References 28 publications

Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

Discrete Hyperparameter Optimization Model Based on Skewed Distribution

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