Peng-cheng Guo, Jianjun Zhao, Zizhuan Li, Xianda Ni, Daohuan Tan, W. Bao
The occurrence of forest fire has caused a large area of forest damage, casualties and economic losses, and forest fire detection is the key to the timely warning of fire. The problem of small target loss still exists in the forest fire detection algorithm using FPN network. In order to solve the problem of poor definition of object edge and loss of small target flame semantic information caused by 32-fold downsampling in FPN multi-scale feature fusion network, a forest fire detection algorithm based on DetNet-FPN feature fusion network was proposed. The backbone network of the algorithm adopts DetNet59, which is specially designed for target detection task. The network is improved on the basis of ResNet50, and the sixth stage is added. In order to maintain the resolution of high-level feature map, downsampling is abandoned in the fifth and sixth stages. Furthermore, dilated convolution is used to replace the original bottleneck structure with 3x3 convolution to enlarge the receptive field of feature map, thus improving the detection ability of small scale targets. Experimental results show that compared with FPN algorithm, the average accuracy of the proposed algorithm is improved by 2.70%, and the accuracy of small target is improved by 2.3%, which has good detection effect in various scenarios.
{"title":"Forest Fire Detection Algorithm Based on DetNet-FPN Feature Fusion Network","authors":"Peng-cheng Guo, Jianjun Zhao, Zizhuan Li, Xianda Ni, Daohuan Tan, W. Bao","doi":"10.1145/3573942.3574032","DOIUrl":"https://doi.org/10.1145/3573942.3574032","url":null,"abstract":"The occurrence of forest fire has caused a large area of forest damage, casualties and economic losses, and forest fire detection is the key to the timely warning of fire. The problem of small target loss still exists in the forest fire detection algorithm using FPN network. In order to solve the problem of poor definition of object edge and loss of small target flame semantic information caused by 32-fold downsampling in FPN multi-scale feature fusion network, a forest fire detection algorithm based on DetNet-FPN feature fusion network was proposed. The backbone network of the algorithm adopts DetNet59, which is specially designed for target detection task. The network is improved on the basis of ResNet50, and the sixth stage is added. In order to maintain the resolution of high-level feature map, downsampling is abandoned in the fifth and sixth stages. Furthermore, dilated convolution is used to replace the original bottleneck structure with 3x3 convolution to enlarge the receptive field of feature map, thus improving the detection ability of small scale targets. Experimental results show that compared with FPN algorithm, the average accuracy of the proposed algorithm is improved by 2.70%, and the accuracy of small target is improved by 2.3%, which has good detection effect in various scenarios.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123647500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The small scale auroral structure is a far less explored field. Provided by auroral images which record the vivid auroral behaviors with satisfied temporal and spatial resolution, we are devoted to studying local auroral motions and the fine scale auroral activities. In order to estimate the auroral motion field, the method of optical flow is introduced to analyze auroral motion. However, the technology requires expensive dense annotations while training the network. Leveraged between the strong learning ability of the fully-supervised deep learning methods and the uncertainty of auroral data, we propose an iterative ground-truth learning approach to mine the pixel-level pseudo ground truth for auroral motion. Specifically, we first train a fully-supervised estimator on synthetic data via the Recurrent All-Pairs Field Transforms (RAFT) algorithm. The reconstructability and robustness of the estimated motion field are used as the criteria to measure applicability of the fully-supervised estimator for auroral images. Then, the mined motion fields as pseudo ground truths are in turn fed into the RAFT algorithms to fine-tune the fully-supervised estimator again, which is iterated until the high-quality pseudo ground truths for auroral data are found. Experiments on auroral data from the Yellow River Station demonstrate the effectiveness of our method. More and more pseudo ground truths of auroral data are used to gradually improve the estimated motion field results by refining the contextual features of auroral images. With iterative pseudo ground truth learning, estimated errors can be reduced effectively.
{"title":"Weakly-Supervised Estimation of Auroral Motion Field Via Iterative Pseudo Ground Truth Learning","authors":"Qianqian Wang, Qiqi Fan, Yanyu Mao","doi":"10.1145/3573942.3574096","DOIUrl":"https://doi.org/10.1145/3573942.3574096","url":null,"abstract":"The small scale auroral structure is a far less explored field. Provided by auroral images which record the vivid auroral behaviors with satisfied temporal and spatial resolution, we are devoted to studying local auroral motions and the fine scale auroral activities. In order to estimate the auroral motion field, the method of optical flow is introduced to analyze auroral motion. However, the technology requires expensive dense annotations while training the network. Leveraged between the strong learning ability of the fully-supervised deep learning methods and the uncertainty of auroral data, we propose an iterative ground-truth learning approach to mine the pixel-level pseudo ground truth for auroral motion. Specifically, we first train a fully-supervised estimator on synthetic data via the Recurrent All-Pairs Field Transforms (RAFT) algorithm. The reconstructability and robustness of the estimated motion field are used as the criteria to measure applicability of the fully-supervised estimator for auroral images. Then, the mined motion fields as pseudo ground truths are in turn fed into the RAFT algorithms to fine-tune the fully-supervised estimator again, which is iterated until the high-quality pseudo ground truths for auroral data are found. Experiments on auroral data from the Yellow River Station demonstrate the effectiveness of our method. More and more pseudo ground truths of auroral data are used to gradually improve the estimated motion field results by refining the contextual features of auroral images. With iterative pseudo ground truth learning, estimated errors can be reduced effectively.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127590778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite quality evaluation method is proposed. This method can judge whether the GPS (Global Positioning System) is interfered by comparing the visual-inertial navigation information with the GPS data information in the geographic coordinate system. This method enables the UAV to quickly switch the navigation mode under the condition of being disturbed, which ensures that the navigation function is accurate and free from interference, and provides a more solid foundation for the navigation of the UAV and other platforms.
{"title":"A New Visual & Inertial Based Satellite Quality Evaluation Method","authors":"Pi Qiao, Ruichen Wu, Lei Sun, Dongfang Yang","doi":"10.1145/3573942.3573994","DOIUrl":"https://doi.org/10.1145/3573942.3573994","url":null,"abstract":"Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite quality evaluation method is proposed. This method can judge whether the GPS (Global Positioning System) is interfered by comparing the visual-inertial navigation information with the GPS data information in the geographic coordinate system. This method enables the UAV to quickly switch the navigation mode under the condition of being disturbed, which ensures that the navigation function is accurate and free from interference, and provides a more solid foundation for the navigation of the UAV and other platforms.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127867720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haoyang Tang, Jiakun Shi, Xin Miao, Ruichen Wu, Dongfang Yang
At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remote sensing image processing with image matching algorithms to propose a GPS-independent visual localization technique for UAVs. First, the VGG16 network is used as the feature extraction backbone network, and the backbone network is designed and optimized for the characteristics of heterogenous remote sensing images. Secondly, a feature point screening and matching strategy is constructed, by which common feature points between heterogeneous remote sensing images can be screened and used for feature matching. Finally, the remote sensing image containing geographic location information and the UAV aerial image are fed into the network for feature extraction and matching, and the transformation matrix between the aligned images is calculated by the successfully matched feature points, and the transformation matrix is used to complete the mapping from the aerial image to the satellite image, and finally the geographic location information of each pixel can be read from the mapped image to complete the localization.
{"title":"UAV Visual Localization Technology Based on Heterogenous Remote Sensing Image Matching","authors":"Haoyang Tang, Jiakun Shi, Xin Miao, Ruichen Wu, Dongfang Yang","doi":"10.1145/3573942.3574094","DOIUrl":"https://doi.org/10.1145/3573942.3574094","url":null,"abstract":"At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remote sensing image processing with image matching algorithms to propose a GPS-independent visual localization technique for UAVs. First, the VGG16 network is used as the feature extraction backbone network, and the backbone network is designed and optimized for the characteristics of heterogenous remote sensing images. Secondly, a feature point screening and matching strategy is constructed, by which common feature points between heterogeneous remote sensing images can be screened and used for feature matching. Finally, the remote sensing image containing geographic location information and the UAV aerial image are fed into the network for feature extraction and matching, and the transformation matrix between the aligned images is calculated by the successfully matched feature points, and the transformation matrix is used to complete the mapping from the aerial image to the satellite image, and finally the geographic location information of each pixel can be read from the mapped image to complete the localization.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124511423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-instance learning (MIL) is the dominant approach for weakly supervised anomaly detection in surveillance videos. The shortcomings of using the features extracted by networks such as Convolutional 3D (C3D) or inflated 3D-ConvNet (I3D) alone to extract video context features have prompted the emergence of various abnormal event detection algorithms based on attention mechanisms. Vision Transformer (ViT) applies transformer to the field of computer vision for the first time and demonstrates its superior performance. In this paper, we propose a multi-instance learning anomaly event detection method based on Transformer, called MIL-ViT, which uses an inflated I3D pre-training model to extract Spatio-temporal features, and then inputs features into the ViT encoder to extract the particular salient pieces of information, and the anomaly scores are obtained. Furthermore, we introduce the MIL ranking loss and the center loss function for better training. The experimental results on two benchmark datasets (i.e. ShanghaiTech and UCF-Crime) show that the AUC value of our method is significantly improved compared with several state-of-the-art methods in recent years.
{"title":"Multi-instance learning anomaly event detection based on Transformer","authors":"Feifei Qin, Yuelei Xiao","doi":"10.1145/3573942.3574104","DOIUrl":"https://doi.org/10.1145/3573942.3574104","url":null,"abstract":"Multi-instance learning (MIL) is the dominant approach for weakly supervised anomaly detection in surveillance videos. The shortcomings of using the features extracted by networks such as Convolutional 3D (C3D) or inflated 3D-ConvNet (I3D) alone to extract video context features have prompted the emergence of various abnormal event detection algorithms based on attention mechanisms. Vision Transformer (ViT) applies transformer to the field of computer vision for the first time and demonstrates its superior performance. In this paper, we propose a multi-instance learning anomaly event detection method based on Transformer, called MIL-ViT, which uses an inflated I3D pre-training model to extract Spatio-temporal features, and then inputs features into the ViT encoder to extract the particular salient pieces of information, and the anomaly scores are obtained. Furthermore, we introduce the MIL ranking loss and the center loss function for better training. The experimental results on two benchmark datasets (i.e. ShanghaiTech and UCF-Crime) show that the AUC value of our method is significantly improved compared with several state-of-the-art methods in recent years.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124515263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Causal inference has been used extensively in health, economics, policy research, and other fields. With the introduction of the Neyman-Rubin framework in 1974, more scholars began to realize that correlation between variables is not equivalent to causation, and therefore, relying too heavily on statistical correlation methods to model can lead to serious theoretical flaws. In this paper, we use data on the work of people with mental illness to analyze whether society treats people with mental illness equally, use propensity score matching (PSM) method to reduce the dimensionality of covariates, and estimate the causal effect of having a mental illness on hiring rates. Our study shows that the covariates can all be well balanced after the implementation of PSM and that employees with mental illness have a 5.8% greater likelihood of leading to dismissal compared to employees in the general population.
{"title":"Can Mental Illness Lead to Dismissal? From a Causal Machine Learning Perspective","authors":"Yuan Feng","doi":"10.1145/3573942.3573950","DOIUrl":"https://doi.org/10.1145/3573942.3573950","url":null,"abstract":"Causal inference has been used extensively in health, economics, policy research, and other fields. With the introduction of the Neyman-Rubin framework in 1974, more scholars began to realize that correlation between variables is not equivalent to causation, and therefore, relying too heavily on statistical correlation methods to model can lead to serious theoretical flaws. In this paper, we use data on the work of people with mental illness to analyze whether society treats people with mental illness equally, use propensity score matching (PSM) method to reduce the dimensionality of covariates, and estimate the causal effect of having a mental illness on hiring rates. Our study shows that the covariates can all be well balanced after the implementation of PSM and that employees with mental illness have a 5.8% greater likelihood of leading to dismissal compared to employees in the general population.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classical Robust Principal Component Analysis (RPCA) uses the singular value threshold operator (SVT) to solve for the convex approximation of the nuclear norm with respect to the rank of a matrix. However, when the matrix size is large, the SVT operator has a slow convergent speed and high computational complexity. To solve the above problems, in this paper, we propose a Robust principal component analysis algorithm based on Global-convergent Iteratively Reweighted Least Squares (RPCA/GIRLS). In the first stage, the low-rank matrix in the original RPCA model is decomposed into two column-sparse matrix factor products, and the two matrix factors are solved via alternating iteratively reweighted least squares algorithms (AIRLS), thus reducing the computational complexity. However, since the AIRLS is sensitive to the initialization, the updated matrix factor in the first stage is used as the new input data matrix in the second stage, and the matrix factor is updated by the gradient descent step, and finally the optimal low-rank matrix that satisfies the global convergent conditions is obtained. We have conducted extensive experiments on six public video data sets, by comparing the background separation effects of these six videos and calculating their quantitative evaluation indexes, the effectiveness and superiority of the proposed algorithm are verified from both subjective and objective perspectives.
{"title":"Robust Principal Component Analysis Based on Globally-convergent Iteratively Reweighted Least Squares","authors":"Weihao Li, Jiu-lun Fan, Xiao-bin Zhi, Xurui Luo","doi":"10.1145/3573942.3574101","DOIUrl":"https://doi.org/10.1145/3573942.3574101","url":null,"abstract":"Classical Robust Principal Component Analysis (RPCA) uses the singular value threshold operator (SVT) to solve for the convex approximation of the nuclear norm with respect to the rank of a matrix. However, when the matrix size is large, the SVT operator has a slow convergent speed and high computational complexity. To solve the above problems, in this paper, we propose a Robust principal component analysis algorithm based on Global-convergent Iteratively Reweighted Least Squares (RPCA/GIRLS). In the first stage, the low-rank matrix in the original RPCA model is decomposed into two column-sparse matrix factor products, and the two matrix factors are solved via alternating iteratively reweighted least squares algorithms (AIRLS), thus reducing the computational complexity. However, since the AIRLS is sensitive to the initialization, the updated matrix factor in the first stage is used as the new input data matrix in the second stage, and the matrix factor is updated by the gradient descent step, and finally the optimal low-rank matrix that satisfies the global convergent conditions is obtained. We have conducted extensive experiments on six public video data sets, by comparing the background separation effects of these six videos and calculating their quantitative evaluation indexes, the effectiveness and superiority of the proposed algorithm are verified from both subjective and objective perspectives.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"20 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116982018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile Edge Computing (MEC) is a promising technology that facilitates the computational offloading and resource allocation in the Internet of Vehicles (IoV) environment. When the mobile device is not capable enough to meet its own demands for data processing, the task will be offloaded to the MEC server, which can effectively relieve the network pressure, meet the multi-task computing requirements, and ensure the quality of service (QoS). Via multi-user and multi-MEC servers, this paper proposes the Q-Learning task offloading strategy based on the improved deep reinforcement learning policy(IDRLP) to obtain an optimal strategy for task offloading and resource allocation. Simulation results suggest that the proposed algorithm compared with other benchmark schemes has better performance in terms of delay, energy consumption and system weighted cost, even with different tasks, users and data sizes.
{"title":"Research on Task Offloading Based on Deep Reinforcement Learning for Internet of Vehicles","authors":"Yaoping Zeng, Yanwei Hu, Ting Yang","doi":"10.1145/3573942.3573987","DOIUrl":"https://doi.org/10.1145/3573942.3573987","url":null,"abstract":"Mobile Edge Computing (MEC) is a promising technology that facilitates the computational offloading and resource allocation in the Internet of Vehicles (IoV) environment. When the mobile device is not capable enough to meet its own demands for data processing, the task will be offloaded to the MEC server, which can effectively relieve the network pressure, meet the multi-task computing requirements, and ensure the quality of service (QoS). Via multi-user and multi-MEC servers, this paper proposes the Q-Learning task offloading strategy based on the improved deep reinforcement learning policy(IDRLP) to obtain an optimal strategy for task offloading and resource allocation. Simulation results suggest that the proposed algorithm compared with other benchmark schemes has better performance in terms of delay, energy consumption and system weighted cost, even with different tasks, users and data sizes.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121796934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In practical engineering design, we often need to carry out spectral analysis of the digital signal, which requires the use of Fourier transform, and it is defined as the spectral analysis of infinite long continuous time-domain signal. Because the computer cannot process and analyze the infinite time signal, it can only calculate the discrete signal of a limited number of points, so it needs to truncate the input signal of system. However, the truncation of signal will cause spectral leakage, resulting in incorrect spectral analysis of the signal. Although the spectral leakage cannot be completely eliminated theoretically, the window function method can suppress its influence. By adding different window functions to the signal, the spectral leakage can be greatly reduced, but the degree of reduction is different. This paper mainly studies the type of different window functions, and the algorithm principle and implementation of window function is realized by using CORDIC algorithm was proposed, by using field programmable logic gate array (FPGA) to complete the real-time signal processing, gives a specific design and implementation, and finished the system function simulation on Vivado platform under Xlinx. The results of MATLAB simulation and system function simulation are compared to verify the feasibility of the design scheme.
{"title":"Design and Implementation of Different Types of Window Functions Based on FPGA","authors":"J. Xue, Dongming Xu, Wei Yang","doi":"10.1145/3573942.3574114","DOIUrl":"https://doi.org/10.1145/3573942.3574114","url":null,"abstract":"In practical engineering design, we often need to carry out spectral analysis of the digital signal, which requires the use of Fourier transform, and it is defined as the spectral analysis of infinite long continuous time-domain signal. Because the computer cannot process and analyze the infinite time signal, it can only calculate the discrete signal of a limited number of points, so it needs to truncate the input signal of system. However, the truncation of signal will cause spectral leakage, resulting in incorrect spectral analysis of the signal. Although the spectral leakage cannot be completely eliminated theoretically, the window function method can suppress its influence. By adding different window functions to the signal, the spectral leakage can be greatly reduced, but the degree of reduction is different. This paper mainly studies the type of different window functions, and the algorithm principle and implementation of window function is realized by using CORDIC algorithm was proposed, by using field programmable logic gate array (FPGA) to complete the real-time signal processing, gives a specific design and implementation, and finished the system function simulation on Vivado platform under Xlinx. The results of MATLAB simulation and system function simulation are compared to verify the feasibility of the design scheme.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122422657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the continuous popularization of the Internet and mobile phones, people have gradually entered a participatory network era, and the rapid growth of social networks has caused an explosion of digital information content. It has turned online opinions, blogs, tweets and posts into highly valuable assets, allowing governments and businesses to gain insights from the data and make their strategies. Business organizations need to process and analyze these sentiments to investigate the data and gain business insights. In recent years, deep learning techniques have been very successful in performing sentiment analysis, which offers automatic feature extraction, rich representation capabilities and better performance compared with traditional feature-based techniques. The core idea is to extract complex features automatically from large amounts of data by building deep neural networks to generate up-to-date predictions. This paper reviews social media sentiment analysis methods based on deep learning. Firstly, it introduces the process of single-modal text sentiment analysis on social media. Then it summarizes the multimodal sentiment analysis algorithms for social media, and divides the algorithm into feature layer fusion, decision layer fusion and linear regression model according to different fusion strategies. finally, the difficulties of social media sentiment analysis based on deep learning and future research directions are discussed.
{"title":"Deep Learning-Based Sentiment Analysis for Social Media","authors":"Zhe Wang, Ying Liu, Jie Fang, Da-xiang Li","doi":"10.1145/3573942.3573947","DOIUrl":"https://doi.org/10.1145/3573942.3573947","url":null,"abstract":"Due to the continuous popularization of the Internet and mobile phones, people have gradually entered a participatory network era, and the rapid growth of social networks has caused an explosion of digital information content. It has turned online opinions, blogs, tweets and posts into highly valuable assets, allowing governments and businesses to gain insights from the data and make their strategies. Business organizations need to process and analyze these sentiments to investigate the data and gain business insights. In recent years, deep learning techniques have been very successful in performing sentiment analysis, which offers automatic feature extraction, rich representation capabilities and better performance compared with traditional feature-based techniques. The core idea is to extract complex features automatically from large amounts of data by building deep neural networks to generate up-to-date predictions. This paper reviews social media sentiment analysis methods based on deep learning. Firstly, it introduces the process of single-modal text sentiment analysis on social media. Then it summarizes the multimodal sentiment analysis algorithms for social media, and divides the algorithm into feature layer fusion, decision layer fusion and linear regression model according to different fusion strategies. finally, the difficulties of social media sentiment analysis based on deep learning and future research directions are discussed.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130863295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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