Dynamic RNN -CNN based Malware Classifier for Deep Learning Algorithm

Cho, Young-Bok

doi:10.1109/itnac46935.2019.9077961

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In the experiment, 300 persons sequencerandomly selected constitutes a training set, while the remaining 300 persons test set. The obtained experimental results are compared with other typical algorithms, the Dynamic RNN-CNN network [11], the accumulative motion context (AMOC) network [12], the algorithm using shared attention of the matrix [13], and the ReRank application method based on shared attention of the matrix [9] . The results of person reidentification rates are shown in table 2.…”

Section: Resultsmentioning

confidence: 99%

Video Person Reidentification based on Neural Ordinary Differential Equations and Graph Convolution Network

DUAN¹,

Huang

2021

Preprint

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Person reidentification rate has become a challenging research topic in the field of computer vision due to the fact that person appearance is easily affected by lighting, posture and perspective. In order to make full use of the continuity of video data on the time line and the unstructured relationship of features, a video person reidentification algorithm combining the neural ordinary differential equation with the graph convolution network is proposed in this paper. First, a continuous time model is constructed by using the ordinary differential equation (ODE) network so as to capture hidden information between video frames. By simulating the hidden space of the hidden variables with the hidden time series model, the hidden information between frames that may be ignored in the discrete model can be obtained. Then, the features of the generated video frames are given to the graph convolution network to reconstruct them. Finally, weak supervision are used to classify the features. Experiments on PRID2011 data sets show that the proposed algorithm can significantly improve person reidentification performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Video Person Reidentification based on Neural Ordinary Differential Equations and Graph Convolution Network

DUAN¹,

Huang

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the experiment, 300 people were randomly selected to form a training set, and the rest 300 people were selected to form a test set. The experimental results are compared with other typical algorithms (dynamic RNN-CNN network [10], cumulative motion context (AMOC) network [11], algorithm using matrix shared attention [12], Note: the step size of unmarked convolution layer is 1 by default. The number after the multiplication symbol in brackets indicates the number of times the submodule in brackets is repeated rearrangement application method based on matrix shared attention [8]).…”

Section: Resultsmentioning

confidence: 99%

Video person reidentification based on neural ordinary differential equations and graph convolution network

Zhang¹,

Huang

Duan

2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Person reidentification rate has become a challenging research topic in the field of computer vision due to the fact that person appearance is easily affected by lighting, posture and perspective. In order to make full use of the continuity of video data on the time line and the unstructured relationship of features, a video person reidentification algorithm combining the neural ordinary differential equation with the graph convolution network is proposed in this paper. First, a continuous time model is constructed by using the ordinary differential equation (ODE) network so as to capture hidden information between video frames. By simulating the hidden space of the hidden variables with the hidden time series model, the hidden information between frames that may be ignored in the discrete model can be obtained. Then, the features of the generated video frames are given to the graph convolution network to reconstruct them. Finally, weak supervision is used to classify the features. Experiments on PRID2011 datasets show that the proposed algorithm can significantly improve person reidentification performance.

show abstract

Dynamic RNN -CNN based Malware Classifier for Deep Learning Algorithm

Cited by 2 publications

References 11 publications

Video Person Reidentification based on Neural Ordinary Differential Equations and Graph Convolution Network

Video Person Reidentification based on Neural Ordinary Differential Equations and Graph Convolution Network

Video person reidentification based on neural ordinary differential equations and graph convolution network

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