Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

Zhang, Qian; Qing-dao-er-ji, Ren; Li, Na

doi:10.1155/2022/3143748

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…In ConvGRU, the full connection part of GRU is turned into a convolution operation, which preserves the ability of GRU to extract time series as well as the ability to process image data and extract its spatial–temporal characteristics. So ConvGRU is applied to video detection ( Wang, Xie & Song, 2018b ), gesture recognition ( Zhao et al, 2022 ) and other fields ( Qian et al, 2022 ).…”

Section: Preliminaries Informationmentioning

confidence: 99%

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Wang

et al. 2023

PeerJ Computer Science

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Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

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Section: Preliminaries Informationmentioning

confidence: 99%

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Wang

et al. 2023

PeerJ Computer Science

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“…As can be seen from Table 2, the user satisfaction rate of the traditional push system is between 0.208 and 0.541 during many tests; The user satisfaction rate of the push system proposed in this paper is between 0.968 and 0.986. It shows that the user satisfaction rate of the proposed marketing push system is significantly better than that of the traditional marketing push system [10][11][12][13][14][15] .…”

Section: Simulation Experimentsmentioning

confidence: 99%

Research on New Media Precision Marketing Method Based on Big Data Information Automatic Push

Zhou¹,

Chen²

2023

Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Chan

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Aiming at the problems of low precision and overload of commodity push system, this paper proposes a precise marketing push model based on user portrait feature information. Based on the analysis results and scoring matrix of users' historical behaviors, the user's interest model is constructed, and it is transformed into the user's label model by using label rules. At the same time, the Thrift framework interface is called according to a variety of mixed rules, and the user's preferred push commodity list is returned, so that an accurate marketing push system with excellent performance and low delay time is realized. The simulation results show that the precision marketing push system in this paper uses NDCG algorithm to optimize the parameters, which has high accuracy and stability.

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Research on Animated GIFs Emotion Recognition Based on ResNet-ConvGRU

Cited by 2 publications

References 24 publications

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Research on New Media Precision Marketing Method Based on Big Data Information Automatic Push

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