Di Yin, Hongbo Zhang, Shih-Mo Yang, R. Yin, Wenjun Zhang
Cell migration assay is the most common research approach for cell migration. Quantitative research and analysis are carried out by measuring the migration of cells into the region that is artificially created among confluent monolayer cells. To improve the efficiency and accuracy of the analysis, the software/tools were developed to assist the image analysis process. However, these software and tools are still at the stage of measuring a single sample, which cannot satisfy the requirement of large sample size for cell migration assay device. In this paper, an image analysis tool based on Fiji is developed, which can segment multiple samples from a scanned image and then analyze a single sample in batch. In addition, the screening function should be added for the application scenario of large sample size. The samples can be filtered according to different conditions to improve the consistency of experimental conditions. The results show that the developed analysis tool ATCA has high accuracy in identifying cell-free zones, with a difference of 2.3% from the tool WHST and 2.9% from manual operation. The analysis efficiency of this tool is 15 times that of manual operation.
{"title":"An image analysis tool for cell migration assay with a large sample size","authors":"Di Yin, Hongbo Zhang, Shih-Mo Yang, R. Yin, Wenjun Zhang","doi":"10.1117/12.2667479","DOIUrl":"https://doi.org/10.1117/12.2667479","url":null,"abstract":"Cell migration assay is the most common research approach for cell migration. Quantitative research and analysis are carried out by measuring the migration of cells into the region that is artificially created among confluent monolayer cells. To improve the efficiency and accuracy of the analysis, the software/tools were developed to assist the image analysis process. However, these software and tools are still at the stage of measuring a single sample, which cannot satisfy the requirement of large sample size for cell migration assay device. In this paper, an image analysis tool based on Fiji is developed, which can segment multiple samples from a scanned image and then analyze a single sample in batch. In addition, the screening function should be added for the application scenario of large sample size. The samples can be filtered according to different conditions to improve the consistency of experimental conditions. The results show that the developed analysis tool ATCA has high accuracy in identifying cell-free zones, with a difference of 2.3% from the tool WHST and 2.9% from manual operation. The analysis efficiency of this tool is 15 times that of manual operation.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121570785","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}
Automatic and accurate arrival time pickup of microseismic first-arrival waves is an important prerequisite for high precision microseismic source location. Aiming at the low efficiency of the traditional manual pickup method and the low accuracy of the long, short window energy ratio (STA/LTA) method commonly used in automatic pickup for low signal-to-noise ratio signals, an automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization is proposed. Firstly, according to the amplitude and energy of microseismic signal and the energy ratio of adjacent time, the signals are marked with different categories. Then the parameters are optimized by particle swarm optimization algorithm to construct the support vector machine model of microseismic first-arrival. Finally, the data is substituted to extract the microseismic first-arrival. The experiment is carried out with the microseismic monitoring data of underground roadway in a gold mine. The experimental results show that, under the condition of low SIGNal-to-noise ratio, the picking accuracy of the proposed method is 96.4%, the average pickup error is 3.9ms, and the picking accuracy and accuracy are better than STA/LTA method.
{"title":"Automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization","authors":"Tieniu Li, Binxin Hu, Zengrong Sun, Feng Zhu, Hua Zhang, Quancheng Yang","doi":"10.1117/12.2667714","DOIUrl":"https://doi.org/10.1117/12.2667714","url":null,"abstract":"Automatic and accurate arrival time pickup of microseismic first-arrival waves is an important prerequisite for high precision microseismic source location. Aiming at the low efficiency of the traditional manual pickup method and the low accuracy of the long, short window energy ratio (STA/LTA) method commonly used in automatic pickup for low signal-to-noise ratio signals, an automatic picking method of microseismic first arrival based on support vector machine based on particle swarm optimization is proposed. Firstly, according to the amplitude and energy of microseismic signal and the energy ratio of adjacent time, the signals are marked with different categories. Then the parameters are optimized by particle swarm optimization algorithm to construct the support vector machine model of microseismic first-arrival. Finally, the data is substituted to extract the microseismic first-arrival. The experiment is carried out with the microseismic monitoring data of underground roadway in a gold mine. The experimental results show that, under the condition of low SIGNal-to-noise ratio, the picking accuracy of the proposed method is 96.4%, the average pickup error is 3.9ms, and the picking accuracy and accuracy are better than STA/LTA method.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125140378","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}
Sign language recognition can make the communication between deaf mutes and healthy people more convenient and fast. In recent years, with the continuous development of deep learning, the research method of deep learning has also been introduced into the field of sign language recognition. Compared with the recognition of isolated words, the recognition of continuous sign language is more time-dependent. The current research still has shortcomings in recognition accuracy. Therefore, we proposed a continuous sign language recognition method based on 3DCNN and BLSTM. Based on the spatial feature information extracted by 3DCNN and the short-term temporal relationship established, the global temporal modeling of the video information of continuous sign language is carried out by using the bidirectional semantic mining ability of BLSTM. The CTC loss function is constructed to solve the problem of time series label misalignment. At the same time, we add the calculation of auxiliary loss function and auxiliary classifier. Experiments show that the auxiliary loss function and classifier can effectively reduce the error rate of the network. The word error rate of the continuous sign language recognition algorithm proposed in this paper on the large continuous sign language dataset RWTH-PHONEIX-Weather 2014 is as low as 23.5%, which is lower than the previous algorithm.
{"title":"Continuous sign language recognition based on 3DCNN and BLSTM","authors":"Hengbo Zhang, Daming Liu, Nana Fu","doi":"10.1117/12.2667649","DOIUrl":"https://doi.org/10.1117/12.2667649","url":null,"abstract":"Sign language recognition can make the communication between deaf mutes and healthy people more convenient and fast. In recent years, with the continuous development of deep learning, the research method of deep learning has also been introduced into the field of sign language recognition. Compared with the recognition of isolated words, the recognition of continuous sign language is more time-dependent. The current research still has shortcomings in recognition accuracy. Therefore, we proposed a continuous sign language recognition method based on 3DCNN and BLSTM. Based on the spatial feature information extracted by 3DCNN and the short-term temporal relationship established, the global temporal modeling of the video information of continuous sign language is carried out by using the bidirectional semantic mining ability of BLSTM. The CTC loss function is constructed to solve the problem of time series label misalignment. At the same time, we add the calculation of auxiliary loss function and auxiliary classifier. Experiments show that the auxiliary loss function and classifier can effectively reduce the error rate of the network. The word error rate of the continuous sign language recognition algorithm proposed in this paper on the large continuous sign language dataset RWTH-PHONEIX-Weather 2014 is as low as 23.5%, which is lower than the previous algorithm.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125644394","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 synthesis of human movement is used in a wide range of applications, such as in military, gaming, sports, medical, robotics and many other fields. The current common approach is based on the acquisition of human motion data by motion capture devices. However, employing this method to gather information on human movements is expensive, time-consuming, and subject to space constraints. In order to avoid these problems, our goal is to create a system that can generate a variety of naturalistic movements quickly and inexpensively. We assume that the creation of human motion is a complicated, non-linear process that is amenable to modeling with deep neural networks. First, using optical motion capture equipment, we collect a range of human motion data, which we then pre-process and annotate. After that-we combine Transformer with Conditional GAN (Cgan) to train this human motion generation model with the collected data. Finally, we evaluate this model by qualitative and qualitative means, which can generate multiple human motions from a high-dimensional potential space based on specified labels.
{"title":"TransCGan-based human motion generator","authors":"Wenya Yu","doi":"10.1117/12.2668277","DOIUrl":"https://doi.org/10.1117/12.2668277","url":null,"abstract":"The synthesis of human movement is used in a wide range of applications, such as in military, gaming, sports, medical, robotics and many other fields. The current common approach is based on the acquisition of human motion data by motion capture devices. However, employing this method to gather information on human movements is expensive, time-consuming, and subject to space constraints. In order to avoid these problems, our goal is to create a system that can generate a variety of naturalistic movements quickly and inexpensively. We assume that the creation of human motion is a complicated, non-linear process that is amenable to modeling with deep neural networks. First, using optical motion capture equipment, we collect a range of human motion data, which we then pre-process and annotate. After that-we combine Transformer with Conditional GAN (Cgan) to train this human motion generation model with the collected data. Finally, we evaluate this model by qualitative and qualitative means, which can generate multiple human motions from a high-dimensional potential space based on specified labels.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131455251","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}
Intelligent anti-jam communication is a new generation of anti-interference technology combined with artificial intelligence, and the identification of interference signals is the basis of the technology. It is required to achieve better identification results with lower computational complexity in engineering applications. However, previous research has shown that they cannot balance these two sides. Here, we report an interference signal identification algorithm based on Extreme Learning Machine (ELM). Five typical oppressive interference signals were recognized based on ELM which is based on feature extraction. The overall correct identification rate is more than 96% under the condition of 40 neurons in a single hidden layer, and it has certain generalization ability. This study objectively promotes the engineering application of this technology.
{"title":"A method for wireless communication interference signal identification based on extreme learning machine","authors":"Xiaozheng Liu, Yue Wang, Xiaofei Wang, Jian Geng","doi":"10.1117/12.2667713","DOIUrl":"https://doi.org/10.1117/12.2667713","url":null,"abstract":"Intelligent anti-jam communication is a new generation of anti-interference technology combined with artificial intelligence, and the identification of interference signals is the basis of the technology. It is required to achieve better identification results with lower computational complexity in engineering applications. However, previous research has shown that they cannot balance these two sides. Here, we report an interference signal identification algorithm based on Extreme Learning Machine (ELM). Five typical oppressive interference signals were recognized based on ELM which is based on feature extraction. The overall correct identification rate is more than 96% under the condition of 40 neurons in a single hidden layer, and it has certain generalization ability. This study objectively promotes the engineering application of this technology.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131516171","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}
Xiuyong Mao, Fan Yang, Kaiyu Fan, Weizhou Hu, Chungui Li
Identifying influential nodes is one of the crucial issues for controlling the network propagation process and exploring network properties in complex networks. Nevertheless, the accuracy of existing methods is still a challenge. In this paper, we rank influential nodes by considering tow aspects. On one hand, a normalized degree centrality is proposed to measure the local influence of each node. On the other hand, an improved fine-grained K-Shell decomposition is defined to measure the spreading ability of neighbors of a node. Further, a novel ranking measure is proposed by combining the normalized degree centrality and fine-grained K-Shell (NDF-FKS). The Susceptible-Infected-Recovery (SIR) model is used to simulate the network propagation process. Experiments with the model are performed on eight synthetic networks and four real networks. The NDF-FKS compared with six measures for accuracy and resolution. The results show that the accuracy of NDF-FKS outperforms existing six measures and has a competitive performance on distinguishing influential nodes.
{"title":"Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell","authors":"Xiuyong Mao, Fan Yang, Kaiyu Fan, Weizhou Hu, Chungui Li","doi":"10.1117/12.2667305","DOIUrl":"https://doi.org/10.1117/12.2667305","url":null,"abstract":"Identifying influential nodes is one of the crucial issues for controlling the network propagation process and exploring network properties in complex networks. Nevertheless, the accuracy of existing methods is still a challenge. In this paper, we rank influential nodes by considering tow aspects. On one hand, a normalized degree centrality is proposed to measure the local influence of each node. On the other hand, an improved fine-grained K-Shell decomposition is defined to measure the spreading ability of neighbors of a node. Further, a novel ranking measure is proposed by combining the normalized degree centrality and fine-grained K-Shell (NDF-FKS). The Susceptible-Infected-Recovery (SIR) model is used to simulate the network propagation process. Experiments with the model are performed on eight synthetic networks and four real networks. The NDF-FKS compared with six measures for accuracy and resolution. The results show that the accuracy of NDF-FKS outperforms existing six measures and has a competitive performance on distinguishing influential nodes.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133162875","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}
Li Huadu, Luo Renze, Tang Xiang, Wu Yong, Li Yalong
There are many subjective influencing factors, poor recognition effect and low efficiency in manual evaluation of pipeline weld defects. An intelligent identification method of pipeline weld defects based on improved DenseNet network is proposed. This method firstly uses the form of multi-channel convolution of different scales to improve the DenseNet network, thereby improving the generalization ability of the network. Then, the feature extraction ability of the network is improved by stacking two convolutions of the same scale. Finally, an attention mechanism module is introduced into the dense connection block of the network to achieve the effect of improving beneficial features and suppressing useless features. The experimental results show that the method can achieve 92% accuracy in the identification of pipeline weld defects, which is about 13% higher than the original method, and has high efficiency, which can fully achieve the purpose of industrial application.
{"title":"Weld defect recognition method based on improved DenseNet","authors":"Li Huadu, Luo Renze, Tang Xiang, Wu Yong, Li Yalong","doi":"10.1117/12.2667731","DOIUrl":"https://doi.org/10.1117/12.2667731","url":null,"abstract":"There are many subjective influencing factors, poor recognition effect and low efficiency in manual evaluation of pipeline weld defects. An intelligent identification method of pipeline weld defects based on improved DenseNet network is proposed. This method firstly uses the form of multi-channel convolution of different scales to improve the DenseNet network, thereby improving the generalization ability of the network. Then, the feature extraction ability of the network is improved by stacking two convolutions of the same scale. Finally, an attention mechanism module is introduced into the dense connection block of the network to achieve the effect of improving beneficial features and suppressing useless features. The experimental results show that the method can achieve 92% accuracy in the identification of pipeline weld defects, which is about 13% higher than the original method, and has high efficiency, which can fully achieve the purpose of industrial application.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114582228","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}
With the frequent occurrence of global security problems, violent crowd behavior endangers public security seriously. Meanwhile, intelligent surveillance video technology can be applied for violent crowd behavior detection as more and more surveillance cameras are installed in public and sensitive areas. In this paper, we propose a novel mean kinetic violent flow (MKViF) algorithm for violent crowd behavior detection by extracting the kinetic energy feature of video flow. Specifically, A is firstly calculating the mean kinetic energy by streak flow of each corner in each frame. Then, we obtain a binary indicator of kinetic energy change by calculating the amplitude change between sequence frames. Finally, the MKViF vector for a sequence of frames is obtained by averaging these binary indicators of each pixel in all frames. Experimental results show that the proposed MKViF algorithm behaves better in classification performance and real-time processing performance (45 frames per second) than the existing algorithms.
{"title":"Detection of violent crowd behavior based on mean kinetic streak flow","authors":"Yin-Chang Zhou","doi":"10.1117/12.2667803","DOIUrl":"https://doi.org/10.1117/12.2667803","url":null,"abstract":"With the frequent occurrence of global security problems, violent crowd behavior endangers public security seriously. Meanwhile, intelligent surveillance video technology can be applied for violent crowd behavior detection as more and more surveillance cameras are installed in public and sensitive areas. In this paper, we propose a novel mean kinetic violent flow (MKViF) algorithm for violent crowd behavior detection by extracting the kinetic energy feature of video flow. Specifically, A is firstly calculating the mean kinetic energy by streak flow of each corner in each frame. Then, we obtain a binary indicator of kinetic energy change by calculating the amplitude change between sequence frames. Finally, the MKViF vector for a sequence of frames is obtained by averaging these binary indicators of each pixel in all frames. Experimental results show that the proposed MKViF algorithm behaves better in classification performance and real-time processing performance (45 frames per second) than the existing algorithms.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115249967","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}
Aiming at the problems that the method based on U-shaped network for medical image segmentation cannot capture the long-range dependencies and could lose some detail information, a multi-scale context-aware segmentation network for medical images is proposed. The model extracts the last three layer features of the encoder, and then introduces a global circular convolution transformer module to solve the problem of long-range dependencies capturing by modeling the global context information. Then, an attention guidance module is introduced to fuse features of different scales, so as to solve the problem of losing details while reducing the introduction of noise information in the low level features. The experimental performance on Synapse multi-organ segmentation datasets indicates that the model produces more precise segmentation results.
{"title":"Multi-scale context-aware segmentation network for medical images","authors":"Qing Li, Yuqing Zhu","doi":"10.1117/12.2667684","DOIUrl":"https://doi.org/10.1117/12.2667684","url":null,"abstract":"Aiming at the problems that the method based on U-shaped network for medical image segmentation cannot capture the long-range dependencies and could lose some detail information, a multi-scale context-aware segmentation network for medical images is proposed. The model extracts the last three layer features of the encoder, and then introduces a global circular convolution transformer module to solve the problem of long-range dependencies capturing by modeling the global context information. Then, an attention guidance module is introduced to fuse features of different scales, so as to solve the problem of losing details while reducing the introduction of noise information in the low level features. The experimental performance on Synapse multi-organ segmentation datasets indicates that the model produces more precise segmentation results.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125496690","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}
Face attribute editing, one of the important research directions in face image synthesis and processing techniques, aims to photorealistic editing single or multiple attributes of face images on demand using editing and generation models. Most existing methods are based on generative adversarial networks, using target attribute vectors to control the editing region or Gaussian noise as conditional input to capture texture details. However, these cannot better control the consistency of attributes in irrelevant regions, while the generation of fidelity is also limited. In this paper, we propose a method that uses an optimized latent space to fuse the attribute feature maps into the latent space. At the same time, make full use of the conditional information for additional constraints. Then, in the image generation phase, we use a progressive architecture for controlled editing of face attributes at different granularities. At last, we also conducted an ablation study on the selected training scheme further to demonstrate the stability and accuracy of our chosen method. The experiments show that our proposed approach, using an end-to-end progressive image translation network architecture, obtained qualitative (FID) as well as quantitative (LPIPS) face image editing results.
{"title":"Embedding diverse features in latent space for face attribute editing","authors":"Rui Yuan, Xiping He, Dan He, Yue Li","doi":"10.1117/12.2667748","DOIUrl":"https://doi.org/10.1117/12.2667748","url":null,"abstract":"Face attribute editing, one of the important research directions in face image synthesis and processing techniques, aims to photorealistic editing single or multiple attributes of face images on demand using editing and generation models. Most existing methods are based on generative adversarial networks, using target attribute vectors to control the editing region or Gaussian noise as conditional input to capture texture details. However, these cannot better control the consistency of attributes in irrelevant regions, while the generation of fidelity is also limited. In this paper, we propose a method that uses an optimized latent space to fuse the attribute feature maps into the latent space. At the same time, make full use of the conditional information for additional constraints. Then, in the image generation phase, we use a progressive architecture for controlled editing of face attributes at different granularities. At last, we also conducted an ablation study on the selected training scheme further to demonstrate the stability and accuracy of our chosen method. The experiments show that our proposed approach, using an end-to-end progressive image translation network architecture, obtained qualitative (FID) as well as quantitative (LPIPS) face image editing results.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125549720","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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