Dynamic measurement via deep learning can be applied in many industrial fields significantly (e.g. electrical power load and fault diagnosis acquisition). Nowadays, accurate and continuous loading measurement is essential in coal mine production. The existing methods are weak in loading measurement because they ignore the symbol characteristics of loading and adjusting features. To address the problem, we propose a hybrid learning method (called ScTCN-LightGBM) to realize the loading measurement of industrial material effectively. First, we provide an abnormal data processing method to guarantee raw data accuracy. Second, we design a sided-composited temporal convolutional network that combines a novel transposed dimensionality-reduction convolution residual block with the conventional residual block. This module can extract symbol characteristics and values of loading and adjusting features well. Finally, we utilize the light-gradient boosting machine to measure loading capacity. Experimental results show that the ScTCN-LightGBM outperforms existing measurement models with high metrics, especially the stability coefficient R2 is 0.923. Compared to the conventional loading measurement method, the measurement performance via ScTCN-LigthGBM improves by 40.2% and the continuous measurement time is 11.28s. This study indicates that the proposed model can achieve the loading measurement of industrial material effectively.
{"title":"ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material","authors":"Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang","doi":"10.1080/09540091.2023.2278275","DOIUrl":"https://doi.org/10.1080/09540091.2023.2278275","url":null,"abstract":"Dynamic measurement via deep learning can be applied in many industrial fields significantly (e.g. electrical power load and fault diagnosis acquisition). Nowadays, accurate and continuous loading measurement is essential in coal mine production. The existing methods are weak in loading measurement because they ignore the symbol characteristics of loading and adjusting features. To address the problem, we propose a hybrid learning method (called ScTCN-LightGBM) to realize the loading measurement of industrial material effectively. First, we provide an abnormal data processing method to guarantee raw data accuracy. Second, we design a sided-composited temporal convolutional network that combines a novel transposed dimensionality-reduction convolution residual block with the conventional residual block. This module can extract symbol characteristics and values of loading and adjusting features well. Finally, we utilize the light-gradient boosting machine to measure loading capacity. Experimental results show that the ScTCN-LightGBM outperforms existing measurement models with high metrics, especially the stability coefficient R2 is 0.923. Compared to the conventional loading measurement method, the measurement performance via ScTCN-LigthGBM improves by 40.2% and the continuous measurement time is 11.28s. This study indicates that the proposed model can achieve the loading measurement of industrial material effectively.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/09540091.2023.2273219
YungYu Zhuang, Ting-Wei Lin, Yin-Jung Huang
{"title":"The algorithm and implementation of an extension to LLVM for solving the blocking between instruction sink and division-modulo combine","authors":"YungYu Zhuang, Ting-Wei Lin, Yin-Jung Huang","doi":"10.1080/09540091.2023.2273219","DOIUrl":"https://doi.org/10.1080/09540091.2023.2273219","url":null,"abstract":"","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/09540091.2023.2272586
Chao-Lin Lee, Chun-Ping Chung, Sheng-Yuan Cheng, Jenq-Kuen Lee, Robert Lai
{"title":"Accelerating AI performance with the incorporation of TVM and MediaTek NeuroPilot","authors":"Chao-Lin Lee, Chun-Ping Chung, Sheng-Yuan Cheng, Jenq-Kuen Lee, Robert Lai","doi":"10.1080/09540091.2023.2272586","DOIUrl":"https://doi.org/10.1080/09540091.2023.2272586","url":null,"abstract":"","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1080/09540091.2023.2267791
Feng Zhou, Xin Du, WenLi Li, Zhihui Lu, Shih-Chia Huang
While population aging has sharply increased the demand for nursing staff, it has also increased the workload of nursing staff. Although some nursing homes use robots to perform part of the work, such robots are the type of robots that perform set tasks. The requirements in actual application scenarios often change, so robots that perform set tasks cannot effectively reduce the workload of nursing staff. In order to provide practical help to nursing staff in nursing homes, we innovatively combine the LightGBM algorithm with the machine learning interpretation framework SHAP (Shapley Additive exPlanations) and use comprehensive data analysis methods to propose a service demand prediction model Fidan (Forecast service demand model). This model analyzes and predicts the demand for elderly services in nursing homes based on relevant health management data (including physiological and sleep data), ward round data, and nursing service data collected by IoT devices. We optimise the model parameters based on Grid Search during the training process. The experimental results show that the Fidan model has an accuracy rate of 86.61% in predicting the demand for elderly services.
人口老龄化在急剧增加对护理人员需求的同时，也增加了护理人员的工作量。尽管一些养老院使用机器人来完成部分工作，但这类机器人是执行固定任务的机器人。实际应用场景中的需求往往会发生变化，因此机器人执行既定任务并不能有效减少护理人员的工作量。为了给养老院的护理人员提供切实的帮助，我们创新地将LightGBM算法与机器学习解释框架SHAP (Shapley Additive explanatory)相结合，运用综合数据分析方法，提出了服务需求预测模型Fidan (Forecast service demand model)。该模型基于相关健康管理数据(包括生理和睡眠数据)、查房数据以及物联网设备收集的护理服务数据，对养老院养老服务需求进行分析和预测。在训练过程中基于网格搜索对模型参数进行优化。实验结果表明，Fidan模型预测养老服务需求的准确率为86.61%。
{"title":"Fidan: a predictive service demand model for assisting nursing home health-care robots","authors":"Feng Zhou, Xin Du, WenLi Li, Zhihui Lu, Shih-Chia Huang","doi":"10.1080/09540091.2023.2267791","DOIUrl":"https://doi.org/10.1080/09540091.2023.2267791","url":null,"abstract":"While population aging has sharply increased the demand for nursing staff, it has also increased the workload of nursing staff. Although some nursing homes use robots to perform part of the work, such robots are the type of robots that perform set tasks. The requirements in actual application scenarios often change, so robots that perform set tasks cannot effectively reduce the workload of nursing staff. In order to provide practical help to nursing staff in nursing homes, we innovatively combine the LightGBM algorithm with the machine learning interpretation framework SHAP (Shapley Additive exPlanations) and use comprehensive data analysis methods to propose a service demand prediction model Fidan (Forecast service demand model). This model analyzes and predicts the demand for elderly services in nursing homes based on relevant health management data (including physiological and sleep data), ward round data, and nursing service data collected by IoT devices. We optimise the model parameters based on Grid Search during the training process. The experimental results show that the Fidan model has an accuracy rate of 86.61% in predicting the demand for elderly services.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136234124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1080/09540091.2023.2272583
Arundhati Sahoo, Asis Kumar Tripathy
{"title":"On routing algorithms in the internet of vehicles: a survey","authors":"Arundhati Sahoo, Asis Kumar Tripathy","doi":"10.1080/09540091.2023.2272583","DOIUrl":"https://doi.org/10.1080/09540091.2023.2272583","url":null,"abstract":"","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136317360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments.
{"title":"Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system","authors":"Yu-Kai Lee, Bo-Yi Lin, Tien-Hsiung Weng, Chien-Kang Huang, Chen Liu, Chih-Chin Liu, Shih-Shun Lin, Han-Ching Wang","doi":"10.1080/09540091.2023.2268878","DOIUrl":"https://doi.org/10.1080/09540091.2023.2268878","url":null,"abstract":"The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1080/09540091.2023.2251717
Song Gao, Hongwei Wang, Yuanjun Zhu, Jiaqi Liu, Ou Tang
Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.
{"title":"Comparative relation mining of customer reviews based on a hybrid CSR method","authors":"Song Gao, Hongwei Wang, Yuanjun Zhu, Jiaqi Liu, Ou Tang","doi":"10.1080/09540091.2023.2251717","DOIUrl":"https://doi.org/10.1080/09540091.2023.2251717","url":null,"abstract":"Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135346338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a foundation task of natural language processing, text classification is widely used in information retrieval, public opinion analysis, and other related tasks. Facing the problem of sparse features of Chinese short texts, which affects the classification accuracy of Chinese short texts, this paper proposes a Chinese short text classification method based on the Character Frequency Sub-word Enhancement (CFSE), which can effectively improve the classification accuracy of Chinese short texts. First, the initial Chinese-character sequence is mapped to the corresponding Character Frequency Sub-word (CFS) sequence based on the global character1 frequency information. Second, the relationship features among data are extracted based on BiLSTM-Att processing CFS sequence, and the semantic features of the initial Chinese-character sequence are obtained through ERNIE. Finally, these two kinds of features are fused and input into the text classifier to obtain the classification results. Experimental results show that the proposed method can improve the classification accuracy of Chinese short texts.
{"title":"CFSE: a Chinese short text classification method based on character frequency sub-word enhancement","authors":"Xingguang Wang, Shunxiang Zhang, Zichen Ma, Yunduo Liu, Youqiang Zhang","doi":"10.1080/09540091.2023.2263663","DOIUrl":"https://doi.org/10.1080/09540091.2023.2263663","url":null,"abstract":"As a foundation task of natural language processing, text classification is widely used in information retrieval, public opinion analysis, and other related tasks. Facing the problem of sparse features of Chinese short texts, which affects the classification accuracy of Chinese short texts, this paper proposes a Chinese short text classification method based on the Character Frequency Sub-word Enhancement (CFSE), which can effectively improve the classification accuracy of Chinese short texts. First, the initial Chinese-character sequence is mapped to the corresponding Character Frequency Sub-word (CFS) sequence based on the global character1 frequency information. Second, the relationship features among data are extracted based on BiLSTM-Att processing CFS sequence, and the semantic features of the initial Chinese-character sequence are obtained through ERNIE. Finally, these two kinds of features are fused and input into the text classifier to obtain the classification results. Experimental results show that the proposed method can improve the classification accuracy of Chinese short texts.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135352481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-04DOI: 10.1080/09540091.2023.2257399
Hao Wang, Dezhi Han, Mingming Cui, Chongqing Chen
Due to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Specifically, NAS-FPN replaces the PAFPN in the baseline YOLOX, greatly enhances the fusion performance of the model’s multi-scale feature information, and a dilated convolution feature enhancement module (DFEM) is designed and integrated into the backbone network to improve the network’s receptive field and target information extraction capabilities. Furthermore, a multi-scale channel-spatial attention (MCSA) mechanism is conceptualised to enhance focus on target regions, improve small-scale target detection, and adapt to multi-scale targets. Additionally, extensive experiments conducted on benchmark datasets, HRSID and SSDD, demonstrate that NAS-YOLOX achieves comparable or superior performance compared to other state-of-the-art ship detection models and reaches best accuracies of 91.1% and 97.2% on AP0.5, respectively.
{"title":"NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention","authors":"Hao Wang, Dezhi Han, Mingming Cui, Chongqing Chen","doi":"10.1080/09540091.2023.2257399","DOIUrl":"https://doi.org/10.1080/09540091.2023.2257399","url":null,"abstract":"Due to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Specifically, NAS-FPN replaces the PAFPN in the baseline YOLOX, greatly enhances the fusion performance of the model’s multi-scale feature information, and a dilated convolution feature enhancement module (DFEM) is designed and integrated into the backbone network to improve the network’s receptive field and target information extraction capabilities. Furthermore, a multi-scale channel-spatial attention (MCSA) mechanism is conceptualised to enhance focus on target regions, improve small-scale target detection, and adapt to multi-scale targets. Additionally, extensive experiments conducted on benchmark datasets, HRSID and SSDD, demonstrate that NAS-YOLOX achieves comparable or superior performance compared to other state-of-the-art ship detection models and reaches best accuracies of 91.1% and 97.2% on AP0.5, respectively.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-04DOI: 10.1080/09540091.2023.2259120
Gang Liu, Qijun Zhou, Xiaoxiao Xie, Qingchen Yu
Although the existing semantic image synthesis methods based on generative adversarial networks (GANs) have achieved great success, the quality of the generated images still cannot achieve satisfactory results. This is mainly caused by two reasons. One reason is that the information in the semantic layout is sparse. Another reason is that a single constraint cannot effectively control the position relationship between objects in the generated image. To address the above problems, we propose a dual-conditional GAN with based on an external attention for semantic image synthesis (DCSIS). In DCSIS, the adaptive normalization method uses the one-hot encoded semantic layout to generate the first latent space and the external attention uses the RGB encoded semantic layout to generate the second latent space. Two latent spaces control the shape of objects and the positional relationship between objects in the generated image. The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. Experiments on several challenging datasets demonstrate the advantages of our method over existing approaches, regarding both visual quality and the representative evaluating criteria.
{"title":"Dual conditional GAN based on external attention for semantic image synthesis","authors":"Gang Liu, Qijun Zhou, Xiaoxiao Xie, Qingchen Yu","doi":"10.1080/09540091.2023.2259120","DOIUrl":"https://doi.org/10.1080/09540091.2023.2259120","url":null,"abstract":"Although the existing semantic image synthesis methods based on generative adversarial networks (GANs) have achieved great success, the quality of the generated images still cannot achieve satisfactory results. This is mainly caused by two reasons. One reason is that the information in the semantic layout is sparse. Another reason is that a single constraint cannot effectively control the position relationship between objects in the generated image. To address the above problems, we propose a dual-conditional GAN with based on an external attention for semantic image synthesis (DCSIS). In DCSIS, the adaptive normalization method uses the one-hot encoded semantic layout to generate the first latent space and the external attention uses the RGB encoded semantic layout to generate the second latent space. Two latent spaces control the shape of objects and the positional relationship between objects in the generated image. The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. Experiments on several challenging datasets demonstrate the advantages of our method over existing approaches, regarding both visual quality and the representative evaluating criteria.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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