The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on E...法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on Encoder-Decoder).在该模型的生成器中,首先将案情要素的关键词序列输入至编码器Encoder阶段的LSTM中编码成一隐含层向量,再将这个隐含层向量输入到解码器Decoder的LSTM中,并结合其各时间步的输出生成下一时间步的隐含层向量,进而得到各时间步的输出,生成文本序列.模型最后采用CNN网络来鉴别生成文本和真实文本之间的差距.实验验证表明,采用所提模型能够生成较理想的法律文本.展开更多
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
文摘法律文本的自动生成能缓解我国法律服务行业中的人力资源不足的问题,对抗生成网络模型的出现为法律文本的自动生成提供了新思路.本文提出一种基于对抗生成网络的文本自动生成模型——ED-GAN(Generative Adversarial Networks based on Encoder-Decoder).在该模型的生成器中,首先将案情要素的关键词序列输入至编码器Encoder阶段的LSTM中编码成一隐含层向量,再将这个隐含层向量输入到解码器Decoder的LSTM中,并结合其各时间步的输出生成下一时间步的隐含层向量,进而得到各时间步的输出,生成文本序列.模型最后采用CNN网络来鉴别生成文本和真实文本之间的差距.实验验证表明,采用所提模型能够生成较理想的法律文本.
