Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy...Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy and visual fidelity.This paper introduces a new deep generative framework,Modified Multi-Scale Fused CycleGAN(MMF-CycleGAN),for robust and photo-realistic profile-to-frontal face synthesis.The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder.The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions,leading to sharper textures and improved global facial geometry.Also,GAN training stability and identity preservation are improved through the Ranger optimizer,which effectively balances adversarial,identity,and cycle-consistency losses.Experiments on three benchmark datasets show that MMFCycleGAN achieves accuracy of 0.9541,0.9455,and 0.9422,F1-scores of 0.9654,0.9641,and 0.9614,and AUC values of 0.9742,0.9714,and 0.9698,respectively,and the extreme-pose accuracy(yaw>60°)reaches 0.92.Despite its enhanced architecture,the framework maintains an efficient inference time of 0.042 s per image,making it suitable for real-time biometric authentication,surveillance,and security applications in unconstrained environments.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
文摘Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy and visual fidelity.This paper introduces a new deep generative framework,Modified Multi-Scale Fused CycleGAN(MMF-CycleGAN),for robust and photo-realistic profile-to-frontal face synthesis.The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder.The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions,leading to sharper textures and improved global facial geometry.Also,GAN training stability and identity preservation are improved through the Ranger optimizer,which effectively balances adversarial,identity,and cycle-consistency losses.Experiments on three benchmark datasets show that MMFCycleGAN achieves accuracy of 0.9541,0.9455,and 0.9422,F1-scores of 0.9654,0.9641,and 0.9614,and AUC values of 0.9742,0.9714,and 0.9698,respectively,and the extreme-pose accuracy(yaw>60°)reaches 0.92.Despite its enhanced architecture,the framework maintains an efficient inference time of 0.042 s per image,making it suitable for real-time biometric authentication,surveillance,and security applications in unconstrained environments.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
